Gene Expression Profiling for Identification, Monitoring and Treatment of Colorectal Cancer

ABSTRACT

A method is provided in various embodiments for determining a profile data set for a subject with colorectal cancer or conditions related to colorectal cancer based on a sample from the subject, wherein the sample provides a source of RNAs. The method includes using amplification for measuring the amount of RNA corresponding to at least 1 constituent from Tables 1-5. The profile data set comprises the measure of each constituent, and amplification is performed under measurement conditions that are substantially repeatable.

REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/858,965 filed Nov. 13, 2006 the contents of which are incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to the identification of biological markers associated with the identification of colorectal cancer. More specifically, the present invention relates to the use of gene expression data in the identification, monitoring and treatment of colorectal cancer and in the characterization and evaluation of conditions induced by or related to colorectal cancer.

BACKGROUND OF THE INVENTION

Colorectal cancer is a type of cancer that develops in the gastrointestinal system (GI system), specifically in the colon, or the rectum. The GI system consists of the small intestine, the large intestine (also known as the colon), the rectum, and the anus. The colon is a muscular tube, about five feet long on average, and has four sections: the ascending colon which begins where the small bowel attaches to the colon and extends upward on the rights side of the abdomen; the transverse colon, which runs across the body from the right to left side in the upper abdomen; the descending colon, which continues downward on the left side; and the sigmoid colon, which joins the rectum, which in turn joins the anus. The wall of each of the sections of the colon and rectum has several layers of tissue. Colorectal cancer starts in the innermost layer of tissue of the colon or rectum and can grow through some or all of the other layers. The stage (i.e., the extent of spread) of colorectal cancer depends on how deeply it invades into these layers.

Colorectal cancer develops slowly over a period of several years, usually beginning as a non-cancerous or pre-cancerous polyp which develops on the lining of the colon or rectum. Certain kinds of polyps, called adenomatous polyps (or adenomas), are highly likely to become cancerous. Other kinds of polyps, called hyperplastic polyps and inflammatory polyps, indicate an increased chance of developing adenomatous polyps and cancer, particularly if growing in the ascending colon. A pre-cancerous condition known as dysplasia is common in people suffering from diseases which cause chronic inflammation in the colon, such as ulcerative colitis or Chrohn's Disease.

Over 95% of colorectal cancers are adenocarcinomas, a cancer of the glandular cells that line the inside layer of the wall of the colon and rectum. Other types of colorectal tumors include carcinoid tumors, which develop from hormone producing cells of the colon; gastrointestinal stromal tumors, which develop in the interstitial cells of Cajal within the wall of the colon; and lymphomas of the digestive system.

Once cancer forms within a colorectal polyp, it eventually grows into the wall of the colon or rectum. Once cancer cells are in the wall, they can grow into blood vessels or lymph vessels, at which point the cancer metastizes.

Colorectal cancer is the third most common cancer diagnosed in men and women, and is the second leading cause of cancer-related deaths in the United States. Risk factors for colorectal cancer include age (increased chance after age 50); personal history of colorectal cancer, polyps, or chronic inflammatory bowel disease; ethnic background (Jews of Eastern European descent have higher rates of colorectal cancer); a diet mostly from animal sources (high in fat); physical inactivity; obesity; smoking (30-40% increased risk for colorectal cancer); and high alcohol intake. Additionally, individuals with a family history of colorectal cancer have an increased risk for developing the disease. About 30% of people who develop colorectal cancer have disease that is familial. About another 10% of people who develop colorectal cancer have an inherited genetic susceptibility to the disease; approximately 3-5% of colorectal cancers are associated with a syndrome called hereditary non-polyposis colorectal cancer (HNPCC), approximately 1% of colorectal cancers are associated with an inherited syndrome called familial adenomatous polyposis (FAP).

FAP is a disease where people develop hundreds of polyps in their colon and rectum, typically between the ages of 5 and 40 years. Cancer develops in one or more of these polyps as early as age 20. By age 40, almost all people with FAP will have developed cancer if preventative surgery is not done. HNPCC also develops at a relatively young age. However, individuals with HNPCC develop only a few polyps. Women with HNPCC have a high risk of developing endometrial cancer. Other cancers associated with HNPCC include cancer of the ovary, stomach, small intestine, pancreas, kidney, ureter, and bile duct. The lifetime risk of developing colorectal cancer for people with HNPCC is about 80%, compared to near 100% for those with FAP.

From the time the first abnormal cells in polyps start to grow, it takes about 10-15 years for them to develop into colorectal cancer. An individual can live asymptomatic for several years with precancerous polyps that develop into colorectal cancer without knowing it. Once symptoms do start presenting, they include changes in bowel habits (e.g., constipation, diarrhea, narrowing of the stool), stomach cramping or bloating, bright red blood in stool, unexplained weight loss, constant fatigue, constant sensation of needing a bowel movement, nausea and vomiting, gaseousness, and anemia.

Treatment of colorectal cancer varies according to type, location, extent, and aggressiveness of the cancer, and can include any one or combination of the following procedures: surgery, radiation therapy, and chemotherapy, and targeted therapy (e.g., monoclonal antibodies). Surgery is the main treatment for colorectal cancer. At early stages it may be possible to remove cancerous polyps through a colonoscope, by passing a wire loop through the colonoscope to cut the polyp from the wall of the colon with an electrical current. The most common operation for colon cancer is a segmental resection, in which the cancer a length of the normal colon on either side of the cancer, and nearby lymph nodes are removed, and the remaining sections of the colon are reattached.

Radiation therapy uses high energy rays to destroy cancer cells, and is used after colorectal surgery to destroy small deposits of cancer that may not be detected during surgery, or when the cancer has attached to an internal organ or lining of the abdomen. Radiation therapy is also used to treat local recurrences of rectal cancer. Several types of radiation therapy are available, including external-beam radiation therapy, endocavitry radiation therapy, and brachytherapy. Radiation therapy is also often used after surgery in combination with chemotherapy.

Chemotherapy can also be used to shrink primary tumors, relieve symptoms of advanced colorectal cancer, or as an adjuvant therapy. Fluorouracil (5-FU) is the drug most often used to treat colon cancer. In adjuvant therapy, it is often administered with leucovorin via an IV injection regimen to increase its effectiveness. Capecitabine (Xeloda™) is an orally administered chemotherapeutic that is converted to 5-FU once it reaches the tumor site. Other chemotherapeutics which have been found to increase the effectiveness 5-FU and leucovorin when given in combination include Irinotecan (Camptosar™), and Oxaliplatin.

Targeted therapies such as monoclonal antibodies are being used more frequently to specifically attack cancer cells with fewer side effects than radiation therapy or chemotherapy. Monoclonal antibodies that have been approved for the treatment of colon cancer include Cetuximab (Erbitux™), and Bevacizumab (Avastin™).

Since individuals with colon cancer can live for several years asymptomatic while the disease progresses, regular screenings are essential to detect colorectal cancer at an early stage, or to prevent abnormal polyps from developing into colorectal cancer. Diagnosis for colorectal cancer is typically done through a combination of a medical history, physical exam, blood tests for anemia or tumor markers (e.g., carcinoembryonic antigen, or CA19-9); and one or more screening methods for polyps or abnormalities in the lining of the colorectal wall.

A number of different screening methods for colorectal cancer are available. However, most procedures are highly invasive and painful. Take home test kits such as the fecal occult blood test (FOBT), or fecal immunochemical test (FIT), use a chemical reaction to detect occult (hidden blood) in the feces due to ruptured blood vessels at the surface of colorectal polyps of adenomas or cancers, damaged by the passage of feces. However, since occult in the stool could be indicative of a variety of gastrointestinal disorders, a colonoscopy or sigmoidoscopy is necessary to verify that positive FOBT or FIT results are due to colorectal cancer.

A colonoscopy involves a colonoscope which is a longer version of a sigmoidoscope, connected to a camera or monitor, and is inserted through the rectum to enable a doctor to visualize the lining of the entire colon. Polyps detected by such screening methods can be removed through a colonoscope or biopsied to determine whether the polyp is cancerous, benign, or a result of inflammation.

Additional screening techniques include invasive imaging techniques such as a barium enema with air contrast, or virtual colonoscopy. A barium enema with air contrast involves pumping barium sulfate and air through the anus to partially fill and open up the colon, then x-ray to image the lining of the colon. Virtual colonoscopy uses only air pumped through the anus to distend the colon, then a helical or spiral CT scan to image the lining of the colon. Ultrasound, CT scan, PET scan, and MRI can also be used to image the lining of the colorectal wall. However, if abnormalities such as polyps are found by any such imaging technique, a procedure such as a colonoscopy or CT guided needle biopsy is still necessary to remove or biopsy the polyp. It is nearly impossible to detect or verify a diagnosis of colorectal cancer in a non-invasive manner, and without causing the patient pain and discomfort. Thus a need exists for better ways to diagnose and monitor the progression and treatment of colorectal cancer.

Additionally, information on any condition of a particular patient and a patient's response to types and dosages of therapeutic or nutritional agents has become an important issue in clinical medicine today not only from the aspect of efficiency of medical practice for the health care industry but for improved outcomes and benefits for the patients. Thus, there is the need for tests which can aid in the diagnosis and monitor the progression and treatment of colorectal cancer.

SUMMARY OF THE INVENTION

The invention is in based in part upon the identification of gene expression profiles (Precision Profiles™) associated with colon cancer. These genes are referred to herein as colon cancer associated genes or colon cancer associated constituents. More specifically, the invention is based upon the surprising discovery that detection of as few as one colon cancer associated gene in a subject derived sample is capable of identifying individuals with or without colon cancer with at least 75% accuracy. More particularly, the invention is based upon the surprising discovery that the methods provided by the invention are capable of detecting colon cancer by assaying blood samples.

In various aspects the invention provides methods of evaluating the presence or absence (e.g., diagnosing or prognosing) of colon cancer, based on a sample from the subject, the sample providing a source of RNAs, and determining a quantitative measure of the amount of at least one constituent of any constituent (e.g., colon cancer associated gene) of any of Tables 1, 2, 3, 4, and 5 and arriving at a measure of each constituent.

Also provided are methods of assessing or monitoring the response to therapy in a subject having colon cancer, based on a sample from the subject, the sample providing a source of RNAs, determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, 5 or 6 and arriving at a measure of each constituent. The therapy, for example, is immunotherapy. Preferably, one or more of the constituents listed in Table 6 is measured. For example, the response of a subject to immunotherapy is monitored by measuring the expression of TNFRSF10A, TMPRSS2, SPARC, ALOX5, PTPRC, PDGFA, PDGFB, BCL2, BAD, BAK1, BAG2, KIT, MUC1, ADAM17, CD19, CD4, CD40LG, CD86, CCR5, CTLA4, HSPA1A, IFNG, IL23A, PTGS2, TLR2, TGFB1, TNF, TNFRSF13B, TNFRSF10B, VEGF, MYC, AURKA, BAX, CDH1, CASP2, CD22, IGF1R, ITGA5, ITGAV, ITGB1, ITGB3, IL6R, JAK1, JAK2, JAK3, MAP3K1, PDGFRA, COX2, PSCA, THBS1, THBS2, TYMS, TLR1, TLR3, TLR6, TLR7, TLR9, TNFSF10, TNFSF13B, TNFRSF17, TP53, ABL1, ABL2, AKT1, KRAS, BRAF, RAF1, ERBB4, ERBB2, ERBB3, AKT2, EGFR, IL12 or IL15. The subject has received an immunotherapeutic drug such as anti CD19 Mab, rituximab, epratuzumab, lumiliximab, visilizumab (Nuvion), HuMax-CD38, zanolimumab, anti CD40 Mab, anti-CD40L, Mab, galiximab anti-CTLA-4 MAb, ipilimumab, ticilimumab, anti-SDF-1 MAb, panitumumab, nimotuzumab, pertuzumab, trastuzumab, catumaxomab, ertumaxomab, MDX-070, anti ICOS, anti IFNAR, AMG-479, anti-IGF-1R Ab, R1507, IMC-A12, antiangiogenesis MAb, CNTO-95, natalizumab (Tysabri), SM3, IPB-01, hPAM-4, PAM4, Imuteran, huBrE-3 tiuxetan, BrevaRex MAb, PDGFR MAb, IMC-3G3, GC-1008, CNTO-148 (Golimumab), CS-1008, belimumab, anti-BAFF MAb, or bevacizumab. Alternatively, the subject has received a placebo.

In a further aspect the invention provides methods of monitoring the progression of colon cancer in a subject, based on a sample from the subject, the sample providing a source of RNAs, by determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent in a sample obtained at a first period of time to produce a first subject data set and determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent in a sample obtained at a second period of time to produce a second subject data set. Optionally, the constituents measured in the first sample are the same constituents measured in the second sample. The first subject data set and the second subject data set are compared allowing the progression of colon cancer in a subject to be determined. The second subject is taken e.g., one day, one week, one month, two months, three months, 1 year, 2 years, or more after the first subject sample. Optionally the first subject sample is taken prior to the subject receiving treatment, e.g. chemotherapy, radiation therapy, or surgery and the second subject sample is taken after treatment.

In various aspects the invention provides a method for determining a profile data set, i.e., a colon cancer profile, for characterizing a subject with colon cancer or conditions related to colon cancer based on a sample from the subject, the sample providing a source of RNAs, by using amplification for measuring the amount of RNA in a panel of constituents including at least 1 constituent from any of Tables 1-5, and arriving at a measure of each constituent. The profile data set contains the measure of each constituent of the panel.

The methods of the invention further include comparing the quantitative measure of the constituent in the subject derived sample to a reference value or a baseline value, e.g. baseline data set. The reference value is for example an index value. Comparison of the subject measurements to a reference value allows for the present or absence of colon cancer to be determined, response to therapy to be monitored or the progression of colon cancer to be determined. For example, a similarity in the subject data set compares to a baseline data set derived form a subject having colon cancer indicates that presence of colon cancer or response to therapy that is not efficacious. Whereas a similarity in the subject data set compares to a baseline data set derived from a subject not having colon cancer indicates the absence of colon cancer or response to therapy that is efficacious. In various embodiments, the baseline data set is derived from one or more other samples from the same subject, taken when the subject is in a biological condition different from that in which the subject was at the time the first sample was taken, with respect to at least one of age, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure, and the baseline profile data set may be derived from one or more other samples from one or more different subjects.

The baseline data set or reference values may be derived from one or more other samples from the same subject taken under circumstances different from those of the first sample, and the circumstances may be selected from the group consisting of (i) the time at which the first sample is taken (e.g., before, after, or during treatment cancer treatment), (ii) the site from which the first sample is taken, (iii) the biological condition of the subject when the first sample is taken.

The measure of the constituent is increased or decreased in the subject compared to the expression of the constituent in the reference, e.g., normal reference sample or baseline value. The measure is increased or decreased 10%, 25%, 50% compared to the reference level. Alternately, the measure is increased or decreased 1, 2, 5 or more fold compared to the reference level.

In various aspects of the invention the methods are carried out wherein the measurement conditions are substantially repeatable, particularly within a degree of repeatability of better than ten percent, five percent or more particularly within a degree of repeatability of better than three percent, and/or wherein efficiencies of amplification for all constituents are substantially similar, more particularly wherein the efficiency of amplification is within ten percent, more particularly wherein the efficiency of amplification for all constituents is within five percent, and still more particularly wherein the efficiency of amplification for all constituents is within three percent or less.

In addition, the one or more different subjects may have in common with the subject at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure. A clinical indicator may be used to assess colon cancer or a condition related to colon cancer of the one or more different subjects, and may also include interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator includes blood chemistry, X-ray or other radiological or metabolic imaging technique, molecular markers in the blood, other chemical assays, and physical findings.

At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50 or more constituents are measured. Preferably; XIN2, C1QA, CDKN2A, CCR7, CNKSR2, C1QB, EGR1, MSH2, MSH6 or RHOC is measured.

In one aspect, two constituents from Table 1 are measured. The first constituent is ACSL5, ALDH1A1, APC, AXIN2, BAX, CA4, CCND3, CD44, CD63, CFLAR, GADD45A, IGFBP4, ITGA3, MGMT, MSH2, or MSH6 and the second constituent is any other constituent from Table 1.

In another aspect two constituents from Table 2 are measured. The first constituent is ADAM17, ALOX5, APAF1, C1QA, CASP1, CASP3, CCL3, CCL5, CCR5, CD19, CD4, CD8A, CTLA4, CXCL1, CXCR3, DPP4, EGR1, GZMB, HLADRA, HMOX1, HSPA1A, ICAM1, IFI16, IFNG, IL10, IL18, IL18BP, IL1B, IL1R1, IL1RN, IL23A, IL32, IL8, WE1, LTA, MAPK14, MHC2TA, MIF, MMP9, MNDA, MYC, NFB1, PLA2G7, PLAUR, PTGS2, PTPRC, SERPINA1, SSI3, TGFB1, TIMP1, TLR2, TNF, or TNFRSF1A, and the second constituent is any other constituent from Table 2.

In a further aspect two constituents from Table 3 are measured. The first constituent is ABL1, ABL2, AKT1, APAF1, ATM, BAD, BAX, BCL2, BRAF, BRCA1, CASP8, CDK2, CDK4, CDK5, CDKN1A, CDKN2A, CFLAR, COL18A1, E2F1, EGR1, ERBB2, FOS, GZMA, HRAS, IFITM1, IL1B, IL8, ITGA1, ITGA3, ITGAE, ITGB1, MMP9, MSH2, MYC, MYCL1, NFKB1, NME4, NOTCH2, NRAS, PCNA, PLAUR, PTCH1, RB1, RHOA, RHOC, S100A4, SEMA4D, SERPINE1, SKI, SKIL, SMAD4, TGFB1, or TNF and the second constituent is any other constituent from Table 3.

In yet another aspect two constituents from Table 4 are measured. The first constituent is, CEBPB, CREBBP, EGR1, EGR2, FOS, ICAM1, MAP2K1, NAB1, NKB1, NR4A2, SRC, TGFB1, and TOPBP1 and the second constituent is from the group consisting of NAB1, NR4A2, PDGFA, PTEN, TGFB1, TNFRSF6, or TOPBP1, and the second constituent is any other constituent from Table 4.

In a further aspect two constituents from Table 5 are measured. The first constituent is ADAM17, APC, AXIN2, BAX, BCAM, C1QA, C1QB, CA4, CASP9, CAV1, CCL3, CCL5, CCR7, CD59, CD97, CNKSR2, CTNNA1, CTSD, DAD1, DIABLO, E2F1, EGR1, ESR1, ETS2, FOS, G6PD, GNB1, GSK3B, HMGA1, HMOX1, HOXA10, IFI16, IGF2BP2, IKBKE, IL8, ING2, IQGAP1, IRF1, ITGAL, LARGE, LGALS8, LTA, MAPK14, MLH1, MME, MMP9, MNDA, MSH2, MSH6, MTA1, MTF1, MYD88, NBEA, NCOA1, NRAS, PLEK2, PLXDC2, PTEN, PTPRK, RBM5, S100A4, SERPINE1, SERPING1, SIAH2, SPARC, SRF, ST14, TGFB1, TIMP1, TLR2, TNF, TNFRSF1A, TNFSF5, or UBE2C and the second constituent is any other constituent from Table 5.

The panel of constituents are selected so as to distinguish from a normal and a colorectal cancer-diagnosed subject. The colorectal cancer-diagnosed subject is diagnosed with different stages of cancer. Alternatively, the panel of constituents is selected as to permit characterizing the severity of colon cancer in relation to a normal subject over time so as to track movement toward normal as a result of successful therapy and away from normal in response to cancer recurrence. Thus in some embodiments, the methods of the invention are used to determine efficacy of treatment of a particular subject.

Preferably, the constituents are selected so as to distinguish, e.g., classify between a normal and a colon cancer-diagnosed subject with at least 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater accuracy. By “accuracy” is meant that the method has the ability to distinguish, e.g., classify, between subjects having colon cancer or conditions associated with colon cancer, and those that do not. Accuracy is determined for example by comparing the results of the Gene Precision Profiling™ to standard accepted clinical methods of diagnosing colorectal cancer, e.g., one or more symptoms of colorectal cancer such changes in bowel habits (e.g., constipation, diarrhea, narrowing of the stool), stomach cramping or bloating, bright red blood in stool, unexplained weight loss, constant fatigue, constant sensation of needing a bowel movement, nausea and vomiting, gaseousness, and anemia.

For example the combination of constituents are selected according to any of the models enumerated in Tables 1A, 2A, 3A, 4A, or 5A.

In some embodiments, the methods of the present invention are used in conjunction with standard accepted clinical methods to diagnose colon cancer. By colorectal cancer or conditions related to colorectal cancer is meant the growth of abnormal cells in the colon or the rectum, capable of invading and destroying other colorectal cells, and includes adenocarcinomas, carcinoid tumors, gastrointestinal stromal tumors, and lymphomas of the digestive system. The term colorectal cancer encompasses both colon cancer and rectal cancer.

The sample is any sample derived from a subject which contains RNA. For example, the sample is blood, a blood fraction, body fluid, a population of cells or tissue from the subject, a colon cell, or a rare circulating tumor cell or circulating endothelial cell found in the blood.

Optionally one or more other samples can be taken over an interval of time that is at least one month between the first sample and the one or more other samples, or taken over an interval of time that is at least twelve months between the first sample and the one or more samples, or they may be taken pre-therapy intervention or post-therapy intervention. In such embodiments, the first sample may be derived from blood and the baseline profile data set may be derived from tissue or body fluid of the subject other than blood. Alternatively, the first sample is derived from tissue or bodily fluid of the subject and the baseline profile data set is derived from blood.

Also included in the invention are kits for the detection of colon cancer in a subject, containing at least one reagent for the detection or quantification of any constituent measured according to the methods of the invention and instructions for using the kit.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical representation of a 2-gene model for cancer based on disease-specific genes, capable of distinguishing between subjects afflicted with cancer and normal subjects with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values above and to the left of the line represent subjects predicted to be in the normal population. Values below and to the right of the line represent subjects predicted to be in the cancer population. ALOX5 values are plotted along the Y-axis, S100A6 values are plotted along the X-axis.

FIG. 2 is a graphical representation of a 2-gene model, MSH6 and PSEN2, based on the Precision Profile™ for Colorectal Cancer (Table 1), capable of distinguishing between subjects afflicted with colon cancer and normal subjects, with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values below and to the right of the line represent subjects predicted to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. MSH6 values are plotted along the Y-axis, PSEN2 values are plotted along the X-axis.

FIG. 3 is a graphical representation of the Z-statistic values for each gene shown in Table 1B. A negative Z statistic means up-regulation of gene expression in colon cancer vs. normal patients; a positive Z statistic means down-regulation of gene expression in colon cancer vs. normal patients.

FIG. 4 is a graphical representation of a colon cancer index based on the 2-gene logistic regression model, MSH6 and PSEN2, capable of distinguishing between normal, healthy subjects and subjects suffering from colon cancer.

FIG. 5 is a graphical representation of a 2-gene model, HMOX1 and TXNRD1, based on the Precision Profile™ for Inflammatory Response (Table 2), capable of distinguishing between subjects afflicted with colon cancer and normal subjects, with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values above and to the left of the line represent subjects predicted to be in the normal population. Values below and to the right of the line represent subjects predicted to be in the colon cancer population. HMOX1 values are plotted along the Y-axis, TXNRD1 values are plotted along the X-axis.

FIG. 6 is a graphical representation of a 2-gene model, ATM and CDKN2A, based on the Human Cancer General Precision Profile™ (Table 3), capable of distinguishing between subjects afflicted with colon cancer and normal subjects, with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values below and to the right of the line represent subjects predicted to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. ATM values are plotted along the Y-axis, CDKN2A values are plotted along the X-axis.

FIG. 7 is a graphical representation of a 2-gene model, AXIN2 and TNF, based on the Cross-Cancer Precision Profile™ (Table 5), capable of distinguishing between subjects afflicted with colon cancer and normal subjects, with a discrimination line overlaid onto the graph as an example of the Index Function evaluated at a particular logit value. Values below and to the right of the line represent subjects predicted to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. AXIN2 values are plotted along the Y-axis, TNF values are plotted along the X-axis.

DETAILED DESCRIPTION Definitions

The following terms shall have the meanings indicated unless the context otherwise requires:

“Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN)) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), or as a likelihood, odds ratio, among other measures.

“Algorithm” is a set of rules for describing a biological condition. The rule set may be defined exclusively algebraically but may also include alternative or multiple decision points requiring domain-specific knowledge, expert interpretation or other clinical indicators.

An “agent” is a “composition” or a “stimulus”, as those terms are defined herein, or a combination of a composition and a stimulus.

“Amplification” in the context of a quantitative RT-PCR assay is a function of the number of DNA replications that are required to provide a quantitative determination of its concentration.

“Amplification” here refers to a degree of sensitivity and specificity of a quantitative assay technique. Accordingly, amplification provides a measurement of concentrations of constituents that is evaluated under conditions wherein the efficiency of amplification and therefore the degree of sensitivity and reproducibility for measuring all constituents is substantially similar.

A “baseline profile data set” is a set of values associated with constituents of a Gene Expression Panel (Precision Profile™) resulting from evaluation of a biological sample (or population-onset of samples) under a desired biological condition that is used for mathematically normative purposes. The desired biological condition may be, for example, the condition of a subject (or population or set of subjects) before exposure to an agent or in the presence of an untreated disease or in the absence of a disease. Alternatively, or in addition, the desired biological condition may be health of a subject or a population or set of subjects. Alternatively, or in addition, the desired biological condition may be that associated with a population or set of subjects selected on the basis of at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure.

A “biological condition” of a subject is the condition of the subject in a pertinent realm that is under observation, and such realm may include any aspect of the subject capable of being monitored for change in condition, such as health; disease including cancer; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity, and mood. As can be seen, a condition in this context may be chronic or acute or simply transient. Moreover, a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood), but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject. The term “biological condition” includes a “physiological condition”.

“Body fluid” of a subject includes blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject.

“Calibrated profile data set” is a function of a member of a first profile data set and a corresponding member of a baseline profile data set for a given constituent in a panel.

A “circulating endothelial cell” (“CEC”) is an endothelial cell from the inner wall of blood vessels which sheds into the bloodstream under certain circumstances, including inflammation, and contributes to the formation of new vasculature associated with cancer pathogenesis. CECs may be useful as a marker of tumor progression and/or response to antiangiogenic therapy.

A “circulating tumor cell” (“CTC”) is a tumor cell of epithelial origin which is shed from the primary tumor upon metastasis, and enters the circulation. The number of circulating tumor cells in peripheral blood is associated with prognosis in patients with metastatic cancer. These cells can be separated and quantified using immunologic methods that detect epithelial cells.

A “clinical indicator” is any physiological datum used alone or in conjunction with other data in evaluating the physiological condition of a collection of cells or of an organism. This term includes pre-clinical indicators.

“Clinical parameters” encompasses all non-sample or non-Precision Profiles™ of a subject's health status or other characteristics, such as, without limitation, age (AGE), ethnicity (RACE), gender (SEX), and family history of cancer.

“Colorectal cancer” is a type of cancer that develops in the colon, or the rectum and includes adenocarcinomas, carcinoid tumors, gastrointestinal stromal tumors, and lymphomas of the digestive system. The term colorectal cancer encompasses both colon cancer and rectal cancer. The terms colorectal cancer and colon cancer are used interchangeably herein.

A “composition” includes a chemical compound, a nutraceutical, a pharmaceutical, a homeopathic formulation, an allopathic formulation, a naturopathic formulation, a combination of compounds, a toxin, a food, a food supplement, a mineral, and a complex mixture of substances, in any physical state or in a combination of physical states.

To “derive” a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel (Precision Profile™) either (i) by direct measurement of such constituents in a biological sample.

“Distinct RNA or protein constituent” in a panel of constituents is a distinct expressed product of a gene, whether RNA or protein. An “expression” product of a gene includes the gene product whether RNA or protein resulting from translation of the messenger RNA.

“FN” is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.

“FP” is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.

A “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, statistical technique, or comparison, that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value.” Non-limiting examples of “formulas” include comparisons to reference values or profiles, sums, ratios, and regression operators, such as coefficients or exponents, value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining constituents of a Gene Expression Panel (Precision Profile™) are linear and non-linear equations and statistical significance and classification analyses to determine the relationship between levels of constituents of a Gene Expression Panel (Precision Profile™) detected in a subject sample and the subject's risk of colorectal cancer. Impanel and combination construction, of particular interest are structural and synactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including, without limitation, such established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression Analysis (LogReg), Kolmogorov Smirnoff tests (KS), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques (CART, LART, LARTree, FlexTree, amongst others), Shrunken Centroids (SC), StepAIC, K-means, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a consituentes of a Gene Expression Panel (Precision Profile™) selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, voting and committee methods, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other clinical studies, or cross-validated within the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV). At various steps, false discovery rates (FDR) may be estimated by value permutation according to techniques known in the art.

A “Gene Expression Panel” (Precision Profile™) is an experimentally verified set of constituents, each constituent being a distinct expressed product of a gene, whether RNA or protein, wherein constituents of the set are selected so that their measurement provides a measurement of a targeted biological condition.

A “Gene Expression Profile™” is a set of values associated with constituents of a Gene Expression Panel (Precision Profile™) resulting from evaluation of a biological sample (or population or set of samples).

A “Gene Expression Profile InflammationIndex” is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of inflammatory condition.

A Gene Expression Profile Cancer Index” is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of a cancerous condition.

The “health” of a subject includes mental, emotional, physical, spiritual, allopathic, naturopathic and homeopathic condition of the subject.

“Index” is an arithmetically or mathematically derived numerical characteristic developed for aid in simplifying or disclosing or informing the analysis of more complex quantitative information. A disease or population index may be determined by the application of a specific algorithm to a plurality of subjects or samples with a common biological condition.

“Inflammation” is used herein in the general medical sense of the word and may be an acute or chronic; simple or suppurative; localized or disseminated; cellular and tissue response initiated or sustained by any number of chemical, physical or biological agents or combination of agents.

“Inflammatory state” is used to indicate the relative biological condition of a subject resulting from inflammation, or characterizing the degree of inflammation.

A “large number” of data sets based on a common panel of genes is a number of data sets sufficiently large to permit a statistically significant conclusion to be drawn with respect to an instance of a data set based on the same panel.

“Negative predictive value” or “NPV” is calculated by TN/(TN+FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

See, e.g., O'Marcaigh A S, Jacobson R M, “Estimating the Predictive Value of a Diagnostic Test, How to Prevent Misleading or Confusing Results,” Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test. Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by Receiver Operating Characteristics (ROC) curves according to Pepe et al., “Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker,” Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, “Clinical Interpretation of Laboratory Procedures,” chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4^(th) edition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., “ROC Curve Analysis: An Example Showing the Relationships Among Serum Lipid and Apolipoprotein Concentrations in Identifying Subjects with Coronory Artery Disease,” Clin. Chem., 1992, 38(8): 1425-1428. An alternative approach using likelihood functions, BIC, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction,” Circulation 2007, 115: 928-935.

A “normal” subject is a subject who is generally in good health, has not been diagnosed with colorectal cancer, is asymptomatic for colorectal cancer, and lacks the traditional laboratory risk factors for colorectal cancer.

A “normative” condition of a subject to whom a composition is to be administered means the condition of a subject before administration, even if the subject happens to be suffering from a disease.

A “panel” of genes is a set of genes including at least two constituents.

A “population of cells” refers to any group of cells wherein there is an underlying commonality or relationship between the members in the population of cells, including a group of cells taken from an organism or from a culture of cells or from a biopsy, for example.

“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

“Risk” in the context of the present invention, relates to the probability that an event will occur over a specific time period, and can mean a subject's “absolute” risk or “relative” risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant timeperiod. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of lower risk cohorts, across population divisions (such as tertiles, quartiles, quintiles, or deciles, etc.) or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(1−p) where p is the probability of event and (1−p) is the probability of no event) to no-conversion.

“Risk evaluation,” or “evaluation of risk” in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, and/or the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to cancer or from cancer remission to cancer, or from primary cancer occurrence to occurrence of a cancer metastasis. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of cancer results, either in absolute or relative terms in reference to a previously measured population. Such differing use may require different consituentes of a Gene Expression Panel (Precision Profile™) combinations and individualized panels, mathematical algorithms, and/or cut-off points, but be subject to the same aforementioned measurements of accuracy and performance for the respective intended use.

A “sample” from a subject may include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from the subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision or intervention or other means known in the art. The sample is blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject. The sample is also a tissue sample. The sample is or contains a circulating endothelial cell or a circulating tumor cell.

“Sensitivity” is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.

“Specificity” is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.

By “statistically significant”, it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a “false positive”). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less and statistically significant at a p-value of 0.10 or less. Such p-values depend significantly on the power of the study performed.

A “set” or “population” of samples or subjects refers to a defined or selected group of samples or subjects wherein there is an underlying commonality or relationship between the members included in the set or population of samples or subjects.

A “Signature Profile™” is an experimentally verified subset of a Gene Expression Profile selected to discriminate a biological condition, agent or physiological mechanism of action.

A “Signature Panel” is a subset of a Gene Expression Panel (Precision Profile™), the constituents of which are selected to permit discrimination of a biological condition, agent or physiological mechanism of action.

A “subject” is a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo or in vitro, under observation. As used herein, reference to evaluating the biological condition of a subject based on a sample from the subject, includes using blood or other tissue sample from a human subject to evaluate the human subject's condition; it also includes, for example, using a blood sample itself as the subject to evaluate, for example, the effect of therapy or an agent upon the sample.

A “stimulus” includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy for seasonal affective disorder, or treatment of psoriasis with psoralen or treatment of cancer with embedded radioactive seeds, other radiation exposure, and (ii) any monitored physical, mental, emotional, or spiritual activity or inactivity of a subject.

“Therapy” includes all interventions whether biological, chemical, physical, metaphysical, or combination of the foregoing, intended to sustain or alter the monitored biological condition of a subject.

“TN” is true negative, which for a disease state test means classifying a non-disease or normal subject correctly.

“TP” is true positive, which for a disease state test means correctly classifying a disease subject.

The PCT patent application publication number WO 01/25473, published Apr. 12, 2001, entitled “Systems and Methods for Characterizing a Biological Condition or Agent Using Calibrated Gene Expression Profiles,” filed for an invention by inventors herein, and which is herein incorporated by reference, discloses the use of Gene Expression Panels (Precision Profiles™) for the evaluation of (i) biological condition (including with respect to health and disease) and (ii) the effect of one or more agents on biological condition (including with respect to health, toxicity, therapeutic treatment and drug interaction).

In particular, the Gene Expression Panels (Precision Profiles™) described herein may be used, without limitation, for measurement of the following: therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted biological conditions; prediction of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or for a population or set of individuals or for a population of cells; determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral or toxic activity; performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status; and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials. These Gene Expression Panels (Precision Profiles™) may be employed with respect to samples derived from subjects in order to evaluate their biological condition.

The present invention provides Gene Expression Panels (Precision Profiles™) for the evaluation or characterization of colorectal cancer and conditions related to colorectal cancer in a subject. In addition, the Gene Expression Panels described herein also provide for the evaluation of the effect of one or more agents for the treatment of colorectal cancer and conditions related to colorectal cancer.

The Gene Expression Panels (Precision Profiles™) are referred to herein as the Precision Profile™ for Colorectal Cancer, the Precision Profile™ for Inflammatory Response, the Human Cancer General Precision Profile™, the Precision Profile™ for EGR1, and the Cross-Cancer Precision Profile™. The Precision Profile™ for Colorectal Cancer includes one or more genes, e.g., constituents, listed in Table 1, whose expression is associated with colorectal cancer or conditions related to colorectal cancer. The Precision Profile™ for Inflammatory Response includes one or more genes, e.g. constituents, listed in Table 2, whose expression is associated with inflammatory response and cancer. The Human Cancer General Precision Profile™ includes one or more genes, e.g., constituents, listed in Table 3, whose expression is associated generally with human cancer (including without limitation prostate, breast, ovarian, cervical, lung, colon, and skin cancer).

The Precision Profile™ for EGR1 includes one or more genes, e.g., constituents listed in Table 4, whose expression is associated with the role early growth response (EGR) gene family plays in human cancer. The Precision Profile™ for EGR1 is composed of members of the early growth response (EGR) family of zinc finger transcriptional regulators; EGR1, 2, 3 & 4 and their binding proteins; NAB1 & NAB2 which function to repress transcription induced by some members of the EGR family of transactivators. In addition to the early growth response genes, The Precision Profile™ for EGR1 includes genes involved in the regulation of immediate early gene expression, genes that are themselves regulated by members of the immediate early gene family (and EGR1 in particular) and genes whose products interact with EGR1, serving as co-activators of transcriptional regulation.

The Cross-Cancer Precision Profile™ includes one or more genes, e.g., constituents listed in Table 5, whose expression has been shown, by latent class modeling, to play a significant role across various types of cancer, including without limitation, prostate, breast, ovarian, cervical, lung, colon, and skin cancer. Each gene of the Precision Profile™ for Colorectal Cancer, the Precision Profile™ for Inflammatory Response, the Human Cancer General Precision Profile™, the Precision Profile™ for EGR1, and the Cross-Cancer Precision Profile™ is referred to herein as a colorectal cancer associated gene or a colorectal cancer associated constituent. In addition to the genes listed in the Precision Profiles™ herein, colorectal cancer associated genes or colorectal cancer associated constituents include oncogenes, tumor suppression genes, tumor progression genes, angiogenesis genes, and lymphogenesis genes.

The present invention also provides a method for monitoring and determining the efficacy of immunotherapy, using the Gene Expression Panels (Precision Profiles™) described herein. Immunotherapy target genes include, without limitation, TNFRSF10A, TMPRSS2, SPARC, ALOX5, PTPRC, PDGFA, PDGFB, BCL2, BAD, BAK1, BAG2, KIT, MUC1, ADAM17, CD19, CD4, CD40LG, CD86, CCR5, CTLA4, HSPA1A, IFNG, IL23A, PTGS2, TLR2, TGFB1, TNF, TNFRSF13B, TNFRSF10B, VEGF, MYC, AURKA, BAX, CDH1, CASP2, CD22, IGF1R, ITGA5, ITGAV, ITGB1, ITGB3, IL6R, JAK1, JAK2, JAK3, MAP3K1, PDGFRA, COX2, PSCA, THBS1, THBS2, TYMS, TLR1, TLR3, TLR6, TLR7, TLR9, TNFSF10, TNFSF13B, TNFRSF17, TP53, ABL1, ABL2, AKT1, KRAS, BRAF, RAF1, ERBB4, ERBB2, ERBB3, AKT2, EGFR, IL12, and IL15. For example, the present invention provides a method for monitoring and determining the efficacy of immunotherapy by monitoring the immunotherapy associated genes, i.e., constituents, listed in Table 6.

It has been discovered that valuable and unexpected results may be achieved when the quantitative measurement of constituents is performed under repeatable conditions (within a degree of repeatability of measurement of better than twenty percent, preferably ten percent or better, more preferably five percent or better, and more preferably three percent or better). For the purposes of this description and the following claims, a degree of repeatability of measurement of better than twenty percent may be used as providing measurement conditions that are “substantially repeatable”. In particular, it is desirable that each time a measurement is obtained corresponding to the level of expression of a constituent in a particular sample, substantially the same measurement should result for substantially the same level of expression. In this manner, expression levels for a constituent in a Gene Expression Panel (Precision Profile™) may be meaningfully compared from sample to sample. Even if the expression level measurements for a particular constituent are inaccurate (for example, say, 30% too low), the criterion of repeatability means that all measurements for this constituent, if skewed, will nevertheless be skewed systematically, and therefore measurements of expression level of the constituent may be compared meaningfully. In this fashion valuable information may be obtained and compared concerning expression of the constituent under varied circumstances.

In addition to the criterion of repeatability, it is desirable that a second criterion also be satisfied, namely that quantitative measurement of constituents is performed under conditions wherein efficiencies of amplification for all constituents are substantially similar as defined herein. When both of these criteria are satisfied, then measurement of the expression level of one constituent may be meaningfully compared with measurement of the expression level of another constituent in a given sample and from sample to sample.

The evaluation or characterization of colorectal cancer is defined to be diagnosing colorectal cancer, assessing the presence or absence of colorectal cancer, assessing the risk of developing colorectal cancer or assessing the prognosis of a subject with colorectal cancer, assessing the recurrence of colorectal cancer or assessing the presence or absence of a metastasis. Similarly, the evaluation or characterization of an agent for treatment of colorectal cancer includes identifying agents suitable for the treatment of colorectal cancer. The agents can be compounds known to treat colorectal cancer or compounds that have not been shown to treat colorectal cancer.

The agent to be evaluated or characterized for the treatment of colorectal cancer may be an alkylating agent (e.g., Cisplatin, Carboplatin, Oxaliplatin, BBR3464, Chlorambucil, Chlormethine, Cyclophosphamides, Ifosmade, Melphalan, Carmustine, Fotemustine, Lomustine, Streptozocin, Busulfan, Dacarbazine, Mechlorethamine, Procarbazine, Temozolomide, ThioTPA, and Uramustine); an anti-metabolite (e.g., purine (azathioprine, mercaptopurine), pyrimidine (Capecitabine, Cytarabine, Fluorouracil, Gemcitabine), and folic acid (Methotrexate, Pemetrexed, Raltitrexed)); a vinca alkaloid (e.g., Vincristine, Vinblastine, Vinorelbine, Vindesine); a taxane (e.g., paclitaxel, docetaxel, BMS-247550); an anthracycline (e.g., Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, Valrubicin, Bleomycin, Hydroxyurea, and Mitomycin); a topoisomerase inhibitor (e.g., Topotecan, Irinotecan Etoposide, and Teniposide); a monoclonal antibody (e.g., Alemtuzumab, Bevacizumab, Cetuximab, Gemtuzumab, Panitumumab, Rituximab, and Trastuzumab); a photosensitizer (e.g., Aminolevulinic acid, Methyl aminolevulinate, Porfimer sodium, and Verteporfin); a tyrosine kinase inhibitor (e.g., Gleevec™); an epidermal growth factor receptor inhibitor (e.g., Iressa™, erlotinib (Tarceva™), gefitinib); an FPTase inhibitor (e.g., FTIs (R115777, SCH66336, L-778,123)); a KDR inhibitor (e.g., SU6668, PTK787); a proteosome inhibitor (e.g., PS341); a TS/DNA synthesis inhibitor (e.g., ZD9331, Raltirexed (ZD1694, Tomudex), ZD9331, 5-FU)); an S-adenosyl-methionine decarboxylase inhibitor (e.g., SAM468A); a DNA methylating agent (e.g., TMZ); a DNA binding agent (e.g., PZA); an agent which binds and inactivates O⁶-alkylguanine AGT (e.g., BG); a c-raf-1 antisense oligo-deoxynucleotide (e.g., ISIS-5132 (CGP-69846A)); tumor immunotherapy (see Table 6); a steroidal and/or non-steroidal anti-inflammatory agent (e.g., corticosteroids, COX-2 inhibitors); or other agents such as Alitretinoin, Altretamine, Amsacrine, Anagrelide, Arsenic trioxide, Asparaginase, Bexarotene, Bortezomib, Celecoxib, Dasatinib, Denileukin Diftitox, Estramustine, Hydroxycarbamide, Imatinib, Pentostatin, Masoprocol, Mitotane, Pegaspargase, and Tretinoin.

Colorectal cancer and conditions related to colorectal cancer is evaluated by determining the level of expression (e.g., a quantitative measure) of an effective number (e.g., one or more) of constituents of a Gene Expression Panel (Precision Profile™) disclosed herein (i.e., Tables 1-5). By an effective number is meant the number of constituents that need to be measured in order to discriminate between a normal subject and a subject having colorectal cancer. Preferably the constituents are selected as to discriminate between a normal subject and a subject having colorectal cancer with at least 75% accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater accuracy.

The level of expression is determined by any means known in the art, such as for example quantitative PCR. The measurement is obtained under conditions that are substantially repeatable. Optionally, the qualitative measure of the constituent is compared to a reference or baseline level or value (e.g. a baseline profile set). In one embodiment, the reference or baseline level is a level of expression of one or more constituents in one or more subjects known not to be suffering from colorectal cancer (e.g., normal, healthy individual(s)). Alternatively, the reference or baseline level is derived from the level of expression of one or more constituents in one or more subjects known to be suffering from colorectal cancer. Optionally, the baseline level is derived from the same subject from which the first measure is derived. For example, the baseline is taken from a subject prior to receiving treatment or surgery for colorectal cancer, or at different time periods during a course of treatment. Such methods allow for the evaluation of a particular treatment for a selected individual. Comparison can be performed on test (e.g., patient) and reference samples (e.g., baseline) measured concurrently or at temporally distinct times. An example of the latter is the use of compiled expression information, e.g., a gene expression database, which assembles information about expression levels of cancer associated genes.

A reference or baseline level or value as used herein can be used interchangeably and is meant to be relative to a number or value derived from population studies, including without limitation, such subjects having similar age range, subjects in the same or similar ethnic group, sex, or, in female subjects, pre-menopausal or post-menopausal subjects, or relative to the starting sample of a subject undergoing treatment for colorectal cancer. Such reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of colorectal cancer. Reference indices can also be constructed and used using algorithms and other methods of statistical and structural classification.

In one embodiment of the present invention, the reference or baseline value is the amount of expression of a cancer associated gene in a control sample derived from one or more subjects who are both asymptomatic and lack traditional laboratory risk factors for colorectal cancer.

In another embodiment of the present invention, the reference or baseline value is the level of cancer associated genes in a control sample derived from one or more subjects who are not at risk or at low risk for developing colorectal cancer.

In a further embodiment, such subjects are monitored and/or periodically retested for a diagnostically relevant period of time (“longitudinal studies”) following such test to verify continued absence from colorectal cancer (disease or event free survival). Such period of time may be one year, two years, two to five years, five years, five to ten years, ten years, or ten or more years from the initial testing date for determination of the reference or baseline value. Furthermore, retrospective measurement of cancer associated genes in properly banked historical subject samples may be used in establishing these reference or baseline values, thus shortening the study time required, presuming the subjects have been appropriately followed during the intervening period through the intended horizon of the product claim.

A reference or baseline value can also comprise the amounts of cancer associated genes derived from subjects who show an improvement in cancer status as a result of treatments and/or therapies for the cancer being treated and/or evaluated.

In another embodiment, the reference or baseline value is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of cancer associated genes from one or more subjects who do not have cancer.

For example, where the reference or baseline level is comprised of the amounts of cancer associated genes derived from one or more subjects who have not been diagnosed with colorectal cancer, or are not known to be suffering from colorectal cancer, a change (e.g., increase or decrease) in the expression level of a cancer associated gene in the patient-derived sample as compared to the expression level of such gene in the reference or baseline level indicates that the subject is suffering from or is at risk of developing colorectal cancer. In contrast, when the methods are applied prophylacticly, a similar level of expression in the patient-derived sample of a colorectal cancer associated gene compared to such gene in the baseline level indicates that the subject is not suffering from or is at risk of developing colorectal cancer.

Where the reference or baseline level is comprised of the amounts of cancer associated genes derived from one or more subjects who have been diagnosed with colorectal cancer, or are known to be suffering from colorectal cancer, a similarity in the expression pattern in the patient-derived sample of a colorectal cancer gene compared to the colorectal cancer baseline level indicates that the subject is suffering from or is at risk of developing-colorectal cancer.

Expression of a colorectal cancer gene also allows for the course of treatment of colorectal cancer to be monitored. In this method, a biological sample is provided from a subject undergoing treatment, e.g., if desired, biological samples are obtained from the subject at various time points before, during, or after treatment. Expression of a colorectal cancer gene is then determined and compared to a reference or baseline profile. The baseline profile may be taken or derived from one or more individuals who have been exposed to the treatment. Alternatively, the baseline level may be taken or derived from one or more individuals who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for colorectal cancer and subsequent treatment for colorectal cancer to monitor the progress of the treatment.

Differences in the genetic makeup of individuals can result in differences in their relative abilities to metabolize various drugs. Accordingly, the Precision Profile™ for Colorectal Cancer (Table 1), the Precision Profile™ for Inflammatory Response (Table 2), the Human Cancer General Precision Profile™ (Table 3), the Precision Profile™ for EGR1 (Table 4), and the Cross-Cancer Precision Profile™ (Table 5), disclosed herein, allow for a putative therapeutic or prophylactic to be tested from a selected subject in order to determine if the agent is suitable for treating or preventing colorectal cancer in the subject. Additionally, other genes known to be associated with toxicity may be used. By suitable for treatment is meant determining whether the agent will be efficacious, not efficacious, or toxic for a particular individual. By toxic it is meant that the manifestations of one or more adverse effects of a drug when administered therapeutically. For example, a drug is toxic when it disrupts one or more normal physiological pathways.

To identify a therapeutic that is appropriate for a specific subject, a test sample from the subject is exposed to a candidate therapeutic agent, and the expression of one or more of colorectal cancer genes is determined. A subject sample is incubated in the presence of a candidate agent and the pattern of colorectal cancer gene expression in the test sample is measured and compared to a baseline profile, e.g., a colorectal cancer baseline profile or a non-colorectal cancer baseline profile or an index value. The test agent can be any compound or composition. For example, the test agent is a compound known to be useful in the treatment of colorectal cancer. Alternatively, the test agent is a compound that has not previously been used to treat colorectal cancer.

If the reference sample, e.g., baseline is from a subject that does not have colorectal cancer a similarity in the pattern of expression of colorectal cancer genes in the test sample compared to the reference sample indicates that the treatment is efficacious. Whereas a change in the pattern of expression of colorectal cancer genes in the test sample compared to the reference sample indicates a less favorable clinical outcome or prognosis. By “efficacious” is meant that the treatment leads to a decrease of a sign or symptom of colorectal cancer in the subject or a change in the pattern of expression of a colorectal cancer gene such that the gene expression pattern has an increase in similarity to that of a reference or baseline pattern. Assessment of colorectal cancer is made using standard clinical protocols. Efficacy is determined in association with any known method for diagnosing or treating colorectal cancer.

A Gene Expression Panel (Precision Profile™) is selected in a manner so that quantitative measurement of RNA or protein constituents in the Panel constitutes a measurement of a biological condition of a subject. In one kind of arrangement, a calibrated profile data set is employed. Each member of the calibrated profile data set is a function of (i) a measure of a distinct constituent of a Gene Expression Panel (Precision Profile™) and (ii) a baseline quantity.

Additional embodiments relate to the use of an index or algorithm resulting from quantitative measurement of constituents, and optionally in addition, derived from either expert analysis or computational biology (a) in the analysis of complex data sets; (b) to control or normalize the influence of uninformative or otherwise minor variances in gene expression values between samples or subjects; (c) to simplify the characterization of a complex data set for comparison to other complex data sets, databases or indices or algorithms derived from complex data sets; (d) to monitor a biological condition of a subject; (e) for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted biological conditions; (f) for predictions of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or for a population or set of individuals or for a population of cells; (g) for determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral of toxic activity (h) for performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status and conducting preliminary dosage studies for these patients prior to conducting Phase 1 or 2 trials.

Gene expression profiling and the use of index characterization for a particular condition or agent or both may be used to reduce the cost of Phase 3 clinical trials and may be used beyond Phase 3 trials; labeling for approved drugs; selection of suitable medication in a class of medications for a particular patient that is directed to their unique physiology; diagnosing or determining a prognosis of a medical condition or an infection which may precede onset of symptoms or alternatively diagnosing adverse side effects associated with administration of a therapeutic agent; managing the health care of a patient; and quality control for different batches of an agent or a mixture of agents.

The Subject

The methods disclosed herein may be applied to cells of humans, mammals or other organisms without the need for undue experimentation by one of ordinary skill in the art because all cells transcribe RNA and it is known in the art how to extract RNA from all types of cells.

A subject can include those who have not been previously diagnosed as having colorectal cancer or a condition related to colorectal cancer. Alternatively, a subject can also include those who have already been diagnosed as having colorectal cancer or a condition related to colorectal cancer. Diagnosis of colorectal cancer is made, for example, from any one or combination of the following procedures: a medical history; physical exam; blood tests for anemia or tumor markers (e.g., carcinoembryonic antigen, or CA19-9); and one or more screening methods for polyps or abnormalities in the lining of the colorectal wall. Screening methods for polyps or abnormalities include but are not limited to: digital rectal examination (DRE); fecal occult blood test (FOBT); fecal immunochemical test (FIT); colonoscopy or sigmoidoscopy; barium enema with air contrast; virtual colonoscopy; biopsy (e.g., CT guided needle biopsy); and imaging techniques (e.g., ultrasound, CT scan, PET scan, and MRI).

Optionally, the subject has been previously treated with a surgical procedure for removing colorectal cancer or a condition related to colorectal cancer, including but not limited to any one or combination of the following treatments: laparoscopic surgery, colonic segmental resection, polypectomy and local excision to remove superficial cancer and polyps, local transanal resection, lower anterior or abdominoperineal resection, colo-anal anastomosis, coloplasty, abdominoperineal resection, pelvic exteneration, and urostomy. Optionally, the subject has previously been treated with a therapeutic agent such as radiation therapy (e.g., external beam radiation therapy, endocavitary radiation therapy, and brachytherapy), chemotherapy (e.g., 5-FU, Leucovorin, Capecitabine (Xeloda™), Irinotecan (Camptosar™), and/or Oxaliplatin (Eloxitan™)), and targeted therapies (e.g., Cetuximab (Erbitux™), or Bevacizumab (Avastin™)), alone, in combination, or in succession with a surgical procedure for removing colorectal cancer. Optionally, the subject may be treated with any of the agents previously described; alone, or in combination with a surgical procedure for removing colorectal cancer and/or radiation therapy as previously described.

A subject can also include those who are suffering from, or at risk of developing colorectal cancer or a condition related to colorectal cancer, such as those who exhibit known risk factors for colorectal cancer or conditions related to colorectal cancer. Known risk factors for colorectal cancer include, but are not limited to: age (increased chance after age 50); personal history of colorectal cancer, polyps, or chronic inflammatory bowel disease; ethnic background (Jews of Eastern European descent have higher rates of colorectal cancer); a diet mostly from animal sources (high in fat); physical inactivity; obesity; smoking (30-40% increased risk for colorectal cancer); high alcohol intake; and family history of colorectal cancer, hereditary polyposis colorectal cancer, or familial adenomatous polyposis.

Selecting Constituents of a Gene Expression Panel (Precision Profile™)

The general approach to selecting constituents of a Gene Expression Panel (Precision Profile™) has been described in PCT application publication number WO 01/25473, incorporated herein in its entirety. A wide range of Gene Expression Panels (Precision Profiles™) have been designed and experimentally validated, each panel providing a quantitative measure of biological condition that is derived from a sample of blood or other tissue. For each panel, experiments have verified that a Gene Expression Profile using the panel's constituents is informative of a biological condition. (It has also been demonstrated that in being informative of biological condition, the Gene Expression Profile is used, among other things, to measure the effectiveness of therapy, as well as to provide a target for therapeutic intervention).

In addition to the Precision Profile™ for Colorectal Cancer (Table 1), the Precision Profile™ for Inflammatory Response (Table 2), the Human Cancer General Precision Profile™ (Table 3), the Precision Profile™ for EGR1 (Table 4), and the Cross-Cancer Precision Profile™ (Table 5), include relevant genes which may be selected for a given Precision Profiles™, such as the Precision Profiles™ demonstrated herein to be useful in the evaluation of colorectal cancer and conditions related to colorectal cancer.

Inflammation and Cancer

Evidence has shown that cancer in adults arises frequently in the setting of chronic inflammation. Epidemiological and experimental studies provide strong support for the concept that inflammation facilitates malignant growth. Inflammatory components have been shown to 1) induce DNA damage, which contributes to genetic instability (e.g., cell mutation) and transformed cell proliferation (Balkwill and Mantovani, Lancet 357:539-545 (2001)); 2) promote angiogenesis, thereby enhancing tumor growth and invasiveness (Coussens L. M. and Z. Werb, Nature 429:860-867 (2002)); and 3) impair myelopoiesis and hemopoiesis, which cause immune dysfunction and inhibit immune surveillance (Kusmartsev and Gabrilovic, Cancer Immunol. Immunother. 51:293-298 (2002); Serafini et al., Cancer Immunol. Immunther. 53:64-72 (2004)).

Studies suggest that inflammation promotes malignancy via proinflammatory cytokines, including but not limited to IL-1β, which enhance immune suppression through the induction of myeloid suppressor cells, and that these cells down regulate immune surveillance and allow the outgrowth and proliferation of malignant cells by inhibiting the activation and/or function of tumor-specific lymphocytes. (Bunt et al., J. Immunol. 176: 284-290 (2006). Such studies are consistent with findings that myeloid suppressor cells are found in many cancer patients, including lung and breast cancer, and that chronic inflammation in some of these malignancies may enhance malignant growth (Coussens L. M. and Z. Werb, 2002).

Additionally, many cancers express an extensive repertoire of chemokines and chemokine receptors, and may be characterized by dis-regulated production of chemokines and abnormal chemokine receptor signaling and expression. Tumor-associated chemokines are thought to play several roles in the biology of primary and metastatic cancer such as: control of leukocyte infiltration into the tumor, manipulation of the tumor immune response, regulation of angiogenesis, autocrine or paracrine growth and survival factors, and control of the movement of the cancer cells. Thus, these activities likely contribute to growth within/outside the tumor microenvironment and to stimulate anti-tumor host responses.

As tumors progress, it is common to observe immune deficits not only within cells in the tumor microenvironment but also frequently in the systemic circulation. Whole blood contains representative populations of all the mature cells of the immune system as well as secretory proteins associated with cellular communications. The earliest observable changes of cellular immune activity are altered levels of gene expression within the various immune cell types. Immune responses are now understood to be a rich, highly complex tapestry of cell-cell signaling events driven by associated pathways and cascades—all involving modified activities of gene transcription. This highly interrelated system of cell response is immediately activated upon any immune challenge, including the events surrounding host response to colorectal cancer and treatment. Modified gene expression precedes the release of cytokines and other immunologically important signaling elements.

As such, inflammation genes, such as the genes listed in the Precision Profile™ for Inflammatory Response (Table 2) are useful for distinguishing between subjects suffering from colorectal cancer and normal subjects, in addition to the other gene panels, i.e., Precision Profiles™, described herein.

Early Growth Response Gene Family and Cancer

The early growth response (EGR) genes are rapidly induced following mitogenic stimulation in diverse cell types, including fibroblasts, epithelial cells and B lymphocytes. The EGR genes are members of the broader “Immediate Early Gene” (IEG) family, whose genes are activated in the first round of response to extracellular signals such as growth factors and neurotransmitters, prior to new protein synthesis. The IEG's are well known as early regulators of cell growth and differentiation signals, in addition to playing a role in other cellular processes. Some other well characterized members of the IEG family include the c-myc, c-fos and c-jun oncogenes. Many of the immediate early gene products function as transcription factors and DNA-binding proteins, though other IEG's also include secreted proteins, cytoskeletal proteins and receptor subunits. EGR1 expression is induced by a wide variety of stimuli. It is rapidly induced by mitogens such as platelet derived growth factor (PDGF), fibroblast growth factor (FGF), and epidermal growth factor (EGF), as well as by modified lipoproteins, shear/mechanical stresses, and free radicals. Interestingly, expression of the EGR1 gene is also regulated by the oncogenes v-raf, v-fps and v-src as demonstrated in transfection analysis of cells using promoter-reporter constructs. This regulation is mediated by the serum response elements (SREs) present within the EGR1 promoter region. It has also been demonstrated that hypoxia, which occurs during development of cancers, induces EGR1 expression. EGR1 subsequently enhances the expression of endogenous EGFR, which plays an important role in cell growth (over-expression of EGFR can lead to transformation). Finally, EGR1 has also been shown to be induced by Smad3, a signaling component of the TGFB pathway.

In its role as a transcriptional regulator, the EGR1 protein binds specifically to the G+C rich EGR consensus sequence present within the promoter region of genes activated by EGR1. EGR1 also interacts with additional proteins (CREBBP/EP300) which co-regulate transcription of EGR1 activated genes. Many of the genes activated by EGR1 also stimulate the expression of EGR1, creating a positive feedback loop. Genes regulated by EGR1 include the mitogens: platelet derived growth factor (PDGFA), fibroblast growth factor (FGF), and epidermal growth factor (EGF) in addition to TNF, IL2, PLAU, ICAM1, TP53, ALOX5, PTEN, FN1 and TGFB1.

As such, early growth response genes, or genes associated therewith, such as the genes listed in the Precision Profile™ for EGR1 (Table 4) are useful for distinguishing between subjects suffering from colorectal cancer and normal subjects, in addition to the other gene panels, i.e., Precision Profiles™, described herein.

In general, panels may be constructed and experimentally validated by one of ordinary skill in the art in accordance with the principles articulated in the present application.

Gene Expression Profiles Based on Gene Expression Panels of the Present Invention

Tables 1A-1C were derived from a study of the gene expression patterns described in Example 3 below. Table 1A describes all 1 and 2-gene logistic regression models based on genes from the Precision Profile™ for Colorectal Cancer (Table 1) which are capable of distinguishing between subjects suffering from colorectal cancer and normal subjects with at least 75% accuracy. For example, the first row of Table 1A, describes a 2-gene model, MSH6 and PSEN2, capable of correctly classifying colorectal cancer-afflicted subjects with 84.2% accuracy, and normal subjects with 87.5% accuracy.

Tables 2A-2C were derived from a study of the gene expression patterns described in Example 4 below. Table 2A describes all 1 and 2-gene logistic regression models based on genes from the Precision Profile™ for Inflammatory Response (Table 2), which are capable of distinguishing between subjects suffering from colorectal cancer and normal subjects with at least 75% accuracy. For example, the first row of Table 2A, describes a 2-gene model, HMOX1 and TXNRD1, capable of correctly classifying colorectal cancer-afflicted subjects with 94.4% accuracy, and normal subjects with 93.8% accuracy.

Tables 3A-3C were derived from a study of the gene expression patterns described in Example 5 below. Table 3A describes all 1 and 2-gene logistic regression models based on genes from the Human Cancer General Precision Profile™ (Table 3), which are capable of distinguishing between subjects suffering from colorectal cancer and normal subjects with at least 75% accuracy. For example, the first row of Table 3A, describes a 2-gene model, ATM and CDKN2A, capable of correctly classifying colorectal cancer-afflicted subjects with 91.3% accuracy, and normal subjects with 88% accuracy.

Tables 4A-4B were derived from a study of the gene expression patterns described in Example 6 below. Table 4A describes all 2-gene logistic regression models based on genes from the Precision Profile™ for EGR1 (Table 4), which are capable of distinguishing between subjects suffering from colorectal cancer and normal subjects with at least 75% accuracy. For example, the first row of Table 4A, describes a 2-gene model, NAB2 and TGFB1, capable of correctly classifying colorectal cancer-afflicted subjects with 81.8% accuracy, and normal subjects with 82% accuracy.

Tables 5A-5C were derived from a study of the gene expression patterns described in Example 7 below. Table 5A describes all 1 and 2-gene logistic regression models based on genes from the Cross-Cancer Precision Profile™ (Table 5), which are capable of distinguishing between subjects suffering from colorectal cancer and normal subjects with at least 75% accuracy. For example, the first row of Table 5A, describes a 2-gene model, AXIN2 and TNF, capable of correctly classifying colorectal cancer-afflicted subjects with 90.5% accuracy, and normal subjects with 93.9% accuracy.

Design of Assays

Typically, a sample is run through a panel in replicates of three for each target gene (assay); that is, a sample is divided into aliquots and for each aliquot the concentrations of each constituent in a Gene Expression Panel (Precision Profile™) is measured. From over thousands of constituent assays, with each assay conducted in triplicate, an average coefficient of variation was found (standard deviation/average)*100, of less than 2 percent among the normalized ΔCt measurements for each assay (where normalized quantitation of the target mRNA is determined by the difference in threshold cycles between the internal control (e.g., an endogenous marker such as 18S rRNA, or an exogenous marker) and the gene of interest. This is a measure called “intra-assay variability”. Assays have also been conducted on different occasions using the same sample material. This is a measure of “inter-assay variability”. Preferably, the average coefficient of variation of intra-assay variability or inter-assay variability is less than 20%, more preferably less than 10%, more preferably less than 5%, more preferably less than 4%, more preferably less than 3%, more preferably less than 2%, and even more preferably less than 1%.

It has been determined that it is valuable to use the quadruplicate or triplicate test results to identify and eliminate data points that are statistical “outliers”; such data points are those that differ by a percentage greater, for example, than 3% of the average of all three or four values. Moreover, if more than one data point in a set of three or four is excluded by this procedure, then all data for the relevant constituent is discarded.

Measurement of Gene Expression for a Constituent in the Panel

For measuring the amount of a particular RNA in a sample, methods known to one of ordinary skill in the art were used to extract and quantify transcribed RNA from a sample with respect to a constituent of a Gene Expression Panel (Precision Profile™). (See detailed protocols below. Also see PCT application publication number WO 98/24935 herein incorporated by reference for RNA analysis protocols). Briefly, RNA is extracted from a sample such as any tissue, body fluid, cell (e.g., circulating tumor cell) or culture medium which a population of cells of a subject might be growing. For example, cells may be lysed and RNA eluted in a suitable solution in which to conduct a DNAse reaction. Subsequent to RNA extraction, first strand synthesis may be performed using a reverse transcriptase. Gene amplification, more specifically quantitative PCR assays, can then be conducted and the gene of interest calibrated against an internal marker such as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Any other endogenous marker can be used, such as 28S-25S rRNA and 5S rRNA. Samples are measured in multiple replicates, for example, 3 replicates. In an embodiment of the invention, quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, Calif.). Given a defined efficiency of amplification of target transcripts, the point (e.g., cycle number) that signal from amplified target template is detectable may be directly related to the amount of specific message transcript in the measured sample. Similarly, other quantifiable signals such as fluorescence, enzyme activity, disintegrations per minute, absorbance, etc., when correlated to a known concentration of target templates (e.g., a reference standard curve) or normalized to a standard with limited variability can be used to quantify the number of target templates in an unknown sample.

Although not limited to amplification methods, quantitative gene expression techniques may utilize amplification of the target transcript. Alternatively or in combination with amplification of the target transcript, quantitation of the reporter signal for an internal marker generated by the exponential increase of amplified product may also be used. Amplification of the target template may be accomplished by isothermic gene amplification strategies or by gene amplification by thermal cycling such as PCR.

It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter signal, i.e., internal marker, and the concentration of starting templates. It has been discovered that this objective can be achieved by careful attention to, for example, consistent primer-template ratios and a strict adherence to a narrow permissible level of experimental amplification efficiencies (for example 80.0 to 100%+/−5% relative efficiency, typically 90.0 to 100%+/−5% relative efficiency, more typically 95.0 to 100%+/−2%, and most typically 98 to 100%+/−1% relative efficiency). In determining gene expression levels with regard to a single Gene Expression Profile, it is necessary that all constituents of the panels, including endogenous controls, maintain similar amplification efficiencies, as defined herein, to permit accurate and precise relative measurements for each constituent. Amplification efficiencies are regarded as being “substantially similar”, for the purposes of this description and the following claims, if they differ by no more than approximately 10%, preferably by less than approximately 5%, more preferably by less than approximately 3%, and more preferably by less than approximately 1%. Measurement conditions are regarded as being “substantially repeatable, for the purposes of this description and the following claims, if they differ by no more than approximately +/−10% coefficient of variation (CV), preferably by less than approximately +/−5% CV, more preferably +/−2% CV. These constraints should be observed over the entire range of concentration levels to be measured associated with the relevant biological condition. While it is thus necessary for various embodiments herein to satisfy criteria that measurements are achieved under measurement conditions that are substantially repeatable and wherein specificity and efficiencies of amplification for all constituents are substantially similar, nevertheless, it is within the scope of the present invention as claimed herein to achieve such measurement conditions by adjusting assay results that do not satisfy these criteria directly, in such a manner as to compensate for errors, so that the criteria are satisfied after suitable adjustment of assay results.

In practice, tests are run to assure that these conditions are satisfied. For example, the design of all primer-probe sets are done in house, experimentation is performed to determine which set gives the best performance. Even though primer-probe design can be enhanced using computer techniques known in the art, and notwithstanding common practice, it has been found that experimental validation is still useful. Moreover, in the course of experimental validation, the selected primer-probe combination is associated with a set of features:

The reverse primer should be complementary to the coding DNA strand. In one embodiment, the primer should be located across an intron-exon junction, with not more than four bases of the three-prime end of the reverse primer complementary to the proximal exon. (If more than four bases are complementary, then it would tend to competitively amplify genomic DNA.)

In an embodiment of the invention, the primer probe set should amplify cDNA of less than 110 bases in length and should not amplify, or generate fluorescent signal from, genomic DNA or transcripts or cDNA from related but biologically irrelevant loci.

A suitable target of the selected primer probe is first strand cDNA, which in one embodiment may be prepared from whole blood as follows:

(a) Use of Whole Blood for Ex Vivo Assessment of a Biological Condition

Human blood is obtained by venipuncture and prepared for assay. The aliquots of heparinized, whole blood are mixed with additional test therapeutic compounds and held at 37° C. in an atmosphere of 5% CO₂ for 30 minutes. Cells are lysed and nucleic acids, e.g., RNA, are extracted by various standard means.

Nucleic acids, RNA and or DNA, are purified from cells, tissues or fluids of the test population of cells. RNA is preferentially obtained from the nucleic acid mix using a variety of standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in the present using a filter-based RNA isolation system from Ambion (RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Tex.).

(b) Amplification Strategies.

Specific RNAs are amplified using message specific primers or random primers. The specific primers are synthesized from data obtained from public databases (e.g., Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.), including information from genomic and cDNA libraries obtained from humans and other animals. Primers are chosen to preferentially amplify from specific RNAs obtained from the test or indicator samples (see, for example, RT PCR, Chapter 15 in RNA Methodologies, A Laboratory Guide for Isolation and Characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press; or Chapter 22 pp. 143-151, RNA Isolation and Characterization Protocols, Methods in Molecular Biology, Volume 86, 1998, R. Rapley and D. L. Manning Eds., Human Press, or Chapter 14 Statistical refinement of primer design parameters; or Chapter 5, pp. 55-72, PCR Applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press). Amplifications are carried out in either isothermic conditions or using a thermal cycler (for example, a ABI 9600 or 9700 or 7900 obtained from Applied Biosystems, Foster City, Calif.; see Nucleic acid detection methods, pp. 1-24, in Molecular Methods for Virus Detection, D. L. Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acids are detected using fluorescent-tagged detection oligonucleotide probes (see, for example, Taqman™ PCR Reagent Kit, Protocol, part number 402823, Revision A, 1996, Applied Biosystems, Foster City Calif.) that are identified and synthesized from publicly known databases as described for the amplification primers.

For example, without limitation, amplified cDNA is detected and quantified using detection systems such as the ABI Prism®7900 Sequence Detection System (Applied Biosystems (Foster City, Calif.)), the Cepheid SmartCycler® and Cepheid GeneXpert® Systems, the Fluidigm BioMark™ System, and the Roche LightCycler® 480 Real-Time PCR System. Amounts of specific RNAs contained in the test sample can be related to the relative quantity of fluorescence observed (see for example, Advances in Quantitative PCR Technology: 5′ Nuclease Assays, Y. S. Lie and C. J. Petropolus, Current Opinion in Biotechnology, 1998, 9:43-48, or Rapid Thermal Cycling and PCR Kinetics, pp. 211-229, chapter 14 in PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press). Examples of the procedure used with several of the above-mentioned detection systems are described below. In some embodiments, these procedures can be used for both whole blood RNA and RNA extracted from cultured cells (e.g., without limitation, CTCs, and CECs). In some embodiments, any tissue, body fluid, or cell(s) (e.g., circulating tumor cells (CTCs) or circulating endothelial cells (CECs)) may be used for ex vivo assessment of a biological condition affected by an agent. Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent (see WO 98/24935 herein incorporated by reference).

An example of a procedure for the synthesis of first strand cDNA for use in PCR amplification is as follows:

Materials

1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or equivalent).

Methods

1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice immediately. All other reagents can be thawed at room temperature and then placed on ice.

2. Remove RNA samples from −80° C. freezer and thaw at room temperature and then place immediately on ice.

3. Prepare the following cocktail of Reverse Transcriptase Reagents for each 100 mL RT reaction (for multiple samples, prepare extra cocktail to allow for pipetting error):

1 reaction (mL) 11X, e.g. 10 samples (μL) 10X RT Buffer 10.0 110.0 25 mM MgCl₂ 22.0 242.0 dNTPs 20.0 220.0 Random Hexamers 5.0 55.0 RNAse Inhibitor 2.0 22.0 Reverse Transcriptase 2.5 27.5 Water 18.5 203.5 Total: 80.0 880.0 (80 μL per sample)

4. Bring each RNA sample to a total volume of 20 μL in a 1.5 mL microcentrifuge tube (for example, remove 10 μL RNA and dilute to 20 μL with RNase/DNase free water, for whole blood RNA use 20 μL total RNA) and add 80 μL RT reaction mix from step 5.2.3. Mix by pipetting up and down.

5. Incubate sample at room temperature for 10 minutes.

6. Incubate sample at 37° C. for 1 hour.

7. Incubate sample at 90° C. for 10 minutes.

8. Quick spin samples in microcentrifuge.

9. Place sample on ice if doing PCR immediately, otherwise store sample at −20° C. for future use.

10. PCR QC should be run on all RT samples using 18S and β-actin.

Following the synthesis of first strand cDNA, one particular embodiment of the approach for amplification of first strand cDNA by PCR, followed by detection and quantification of constituents of a Gene Expression Panel (Precision Profile™) is performed using the ABI Prism® 7900 Sequence Detection System as follows:

Materials

1. 20× Primer/Probe Mix for each gene of interest.

2. 20× Primer/Probe Mix for 18S endogenous control.

3. 2× Taqman Universal PCR Master Mix.

4. cDNA transcribed from RNA extracted from cells.

5. Applied Biosystems 96-Well Optical Reaction Plates.

6. Applied Biosystems Optical Caps, or optical-clear film.

7. Applied Biosystem Prism® 7700 or 7900 Sequence Detector.

Methods

1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the gene of interest, Primer/Probe for 18S endogenous control, and 2× PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g., approximately 10% excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).

1X (1 well) (μL)  2X Master Mix 7.5 20X 18S Primer/Probe Mix 0.75 20X Gene of interest Primer/Probe Mix 0.75 Total 9.0

2. Make stocks of cDNA targets by diluting 95 μL of cDNA into 2000 μL of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 16.

3. Pipette 9 μL of Primer/Probe mix into the appropriate wells of an Applied Biosystems 384-Well Optical Reaction Plate.

4. Pipette 10 μL of cDNA stock solution into each well of the Applied Biosystems 384-Well Optical Reaction Plate.

5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.

6. Analyze the plate on the ABI Prism®7900 Sequence Detector.

In another embodiment of the invention, the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile™) is performed using a QPCR assay on Cepheid SmartCycler® and GeneXpert® Instruments as follows:

-   I. To run a QPCR assay in duplicate on the Cepheid SmartCycler®     instrument containing three target genes and one reference gene, the     following procedure should be followed.

A. With 20× Primer/Probe Stocks.

Materials

-   -   1. SmartMix™-HM lyophilized Master Mix.     -   2. Molecular grade water.     -   3. 20× Primer/Probe Mix for the 18S endogenous control gene. The         endogenous control gene will be dual labeled with VIC-MGB or         equivalent.     -   4. 20× Primer/Probe Mix for each for target gene one, dual         labeled with FAM-BHQ1 or equivalent.     -   5. 20× Primer/Probe Mix for each for target gene two, dual         labeled with Texas Red-BHQ2 or equivalent.     -   6. 20× Primer/Probe Mix for each for target gene three, dual         labeled with Alexa 647-BHQ3 or equivalent.     -   7. Tris buffer, pH 9.0     -   8. cDNA transcribed from RNA extracted from sample.     -   9. SmartCycler® 25 μL tube.     -   10. Cepheid SmartCycler® instrument.

Methods

-   -   1. For each cDNA sample to be investigated, add the following to         a sterile 650 μL tube.

SmartMix ™-HM lyophilized Master Mix 1 bead 20X 18S Primer/Probe Mix 2.5 μL 20X Target Gene 1 Primer/Probe Mix 2.5 μL 20X Target Gene 2 Primer/Probe Mix 2.5 μL 20X Target Gene 3 Primer/Probe Mix 2.5 μL Tris Buffer, pH 9.0 2.5 μL Sterile Water 34.5 μL Total 47 μL

-   -    Vortex the mixture for 1 second three times to completely mix         the reagents. Briefly centrifuge the tube after vortexing.     -   2. Dilute the cDNA sample so that a 3 μL addition to the reagent         mixture above will give an 18S reference gene CT value between         12 and 16.     -   3. Add 3 μL of the prepared cDNA sample to the reagent mixture         bringing the total volume to 50 μL. Vortex the mixture for 1         second three times to, completely mix the reagents. Briefly         centrifuge the tube after vortexing.     -   4. Add 25 μL of the mixture to each of two SmartCycler® tubes,         cap the tube and spin for 5 seconds in a microcentrifuge having         an adapter for SmartCycler® tubes.     -   5. Remove the two SmartCycler® tubes from the microcentrifuge         and inspect for air bubbles. If bubbles are present, re-spin,         otherwise, load the tubes into the SmartCycler® instrument.     -   6. Run the appropriate QPCR protocol on the SmartCycler®, export         the data and analyze the results.

B. With Lyophilized SmartBeads™.

Materials

-   -   1. SmartMix™-HM lyophilized Master Mix.     -   2. Molecular grade water.     -   3. SmartBeads™ containing the 18S endogenous control gene dual         labeled with VIC-MGB or equivalent, and the three target genes,         one dual labeled with FAM-BHQ1 or equivalent, one dual labeled         with Texas Red-BHQ2 or equivalent and one dual labeled with         Alexa 647-BHQ3 or equivalent.     -   4. Tris buffer, pH 9.0     -   5. cDNA transcribed from RNA extracted from sample.     -   6. SmartCycler® 25 μL tube.     -   7. Cepheid SmartCycler® instrument.

Methods

-   -   1. For each cDNA sample to be investigated, add the following to         a sterile 650 μL tube.

SmartMix ™-HM lyophilized Master Mix 1 bead SmartBead ™ containing four primer/probe sets 1 bead Tris Buffer, pH 9.0 2.5 μL Sterile Water 44.5 μL Total 47 μL

-   -    Vortex the mixture for 1 second three times to completely mix         the reagents. Briefly centrifuge the tube after vortexing.     -   2. Dilute the cDNA sample so that a 3 μL addition to the reagent         mixture above will give an 18S reference gene CT value between         12 and 16.,     -   3. Add 3 μL of the prepared cDNA sample to the reagent mixture         bringing the total volume to 50 μL. Vortex the mixture for 1         second three times to completely mix the reagents. Briefly         centrifuge the tube after vortexing.     -   4. Add 25 μL of the mixture to each of two SmartCycler® tubes,         cap the tube and spin for 5 seconds in a microcentrifuge having         an adapter for SmartCycler® tubes.     -   5. Remove the two SmartCycler®tubes from the microcentrifuge and         inspect for air bubbles. If bubbles are present, re-spin,         otherwise, load the tubes into the SmartCycler® instrument.     -   6. Run the appropriate QPCR protocol on the SmartCycler®, export         the data and analyze the results.

-   II. To run a QPCR assay on the Cepheid GeneXpert® instrument     containing three target genes and one reference gene, the following     procedure should be followed. Note that to do duplicates, two self     contained cartridges need to be loaded and run on the GeneXpert®     instrument.

Materials

-   -   1. Cepheid GeneXpert® self contained cartridge preloaded with a         lyophilized SmartMix™-HM master mix bead and a lyophilized         SmartBead™ containing four primer/probe sets.     -   2. Molecular grade water, containing Tris buffer, pH 9.0.     -   3. Extraction and purification reagents.     -   4. Clinical sample (whole blood, RNA, etc.)     -   5. Cepheid GeneXpert® instrument.

Methods

-   -   1. Remove appropriate GeneXpert® self contained cartridge from         packaging.     -   2. Fill appropriate chamber of self contained cartridge with         molecular grade water with Tris buffer, pH 9.0.     -   3. Fill appropriate chambers of self contained cartridge with         extraction and purification reagents.     -   4. Load aliquot of clinical sample into appropriate chamber of         self contained cartridge.     -   5. Seal cartridge and load into GeneXpert®instrument.     -   6. Run the appropriate extraction and amplification protocol on         the GeneXpert® and analyze the resultant data.

In yet another embodiment of the invention, the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile™) is performed using a QPCR assay on the Roche LightCycler® 480 Real-Time PCR System as follows:

Materials

-   -   1. 20× Primer/Probe stock for the 18S endogenous control gene.         The endogenous control gene may be dual labeled with either         VIC-MGB or VIC-TAMRA.     -   2. 20× Primer/Probe stock for each target gene, dual labeled         with either FAM-TAMRA or FAM-BHQ1.     -   3. 2× LightCycler® 490 Probes Master (master mix).     -   4. 1× cDNA sample stocks transcribed from RNA extracted from         samples.     -   5. 1× TE buffer, pH 8.0.     -   6. LightCycler® 480 384-well plates.     -   7. Source MDx 24 gene Precision Profile™ 96-well intermediate         plates.     -   8. RNase/DNase free 96-well plate.     -   9. 1.5 mL microcentrifuge tubes.     -   10. Beckman/Coulter Biomek® 3000 Laboratory Automation         Workstation.     -   11. Velocity 11 Bravo™ Liquid Handling Platform.     -   12. LightCycler® 480 Real-Time PCR System.

Methods

-   -   1. Remove a Source MDx 24 gene Precision Profile™ 96-well         intermediate plate from the freezer, thaw and spin in a plate         centrifuge.     -   2. Dilute four (4) 1× cDNA sample stocks in separate 1.5 mL         microcentrifuge tubes with the total final volume for each of         540 μL.     -   3. Transfer the 4 diluted cDNA samples to an empty RNase/DNase         free 96-well plate using the Biomek® 3000 Laboratory Automation         Workstation.     -   4. Transfer the cDNA samples from the cDNA plate created in step         3 to the thawed and centrifuged Source MDx 24 gene Precision         Profile™ 96-well intermediate plate using Biomek® 3000         Laboratory Automation Workstation. Seal the plate with a foil         seal and spin in a plate centrifuge.     -   5. Transfer the contents of the cDNA-loaded Source MDx 24 gene         Precision Profile™ 96-well intermediate plate to a new         LightCycler® 480 384-well plate using the Bravo™ Liquid Handling         Platform. Seal the 384-well plate with a LightCycler® 480         optical sealing foil and spin in a plate centrifuge for 1 minute         at 2000 rpm.     -   6. Place the sealed in a dark 4° C. refrigerator for a minimum         of 4 minutes.     -   7. Load the plate into the LightCycler® 480 Real-Time PCR System         and start the LightCycler® 480 software. Chose the appropriate         run parameters and start the run.     -   8. At the conclusion of the run, analyze the data and export the         resulting CP values to the database.

In some instances, target gene FAM measurements may be beyond the detection limit of the particular platform instrument used to detect and quantify constituents of a Gene Expression Panel (Precision Profile™). To address the issue of “undetermined” gene expression measures as lack of expression for a particular gene, the detection limit may be reset and the “undetermined” constituents may be “flagged”. For example without limitation, the ABI Prism® 7900HT Sequence Detection System reports target gene FAM measurements that are beyond the detection limit of the instrument (>40 cycles) as “undetermined”. Detection Limit Reset is performed when at least 1 of 3 target gene FAM C_(T) replicates are not detected after 40 cycles and are designated as “undetermined”. “Undetermined” target gene FAM C_(T) replicates are re-set to 40 and flagged. C_(T) normalization (Δ C_(T)) and relative expression calculations that have used re-set FAM C_(T) values are also flagged.

Baseline Profile Data Sets

The analyses of samples from single individuals and from large groups of individuals provide a library of profile data sets relating to a particular panel or series of panels. These profile data sets may be stored as records in a library for use as baseline profile data sets. As the term “baseline” suggests, the stored baseline profile data sets serve as comparators for providing a calibrated profile data set that is informative about a biological condition or agent. Baseline profile data sets may be stored in libraries and classified in a number of cross-referential ways. One form of classification may rely on the characteristics of the panels from which the data sets are derived. Another form of classification may be by particular biological condition, e.g., colorectal cancer. The concept of a biological condition encompasses any state in which a cell or population of cells may be found at any one time. This state may reflect geography of samples, sex of subjects or any other discriminator. Some of the discriminators may overlap. The libraries may also be accessed for records associated with a single subject or particular clinical trial. The classification of baseline profile data sets may further be annotated with medical information about a particular subject, a medical condition, and/or a particular agent.

The choice of a baseline profile data set for creating a calibrated profile data set is related to the biological condition to be evaluated, monitored, or predicted, as well as, the intended use of the calibrated panel, e.g., as to monitor drug development, quality control or other uses. It may be desirable to access baseline profile data sets from the same subject for whom a first profile data set is obtained or from different subject at varying times, exposures to stimuli, drugs or complex compounds; or may be derived from like or dissimilar populations or sets of subjects. The baseline profile data set may be normal, healthy baseline.

The profile data set may arise from the same subject for which the first data set is obtained, where the sample is taken at a separate or similar time, a different or similar site or in a different or similar biological condition. For example, a sample may be taken before stimulation or after stimulation with an exogenous compound or substance, such as before or after therapeutic treatment. Alternatively the sample is taken before or include before or after a surgical procedure for colorectal cancer. The profile data set obtained from the unstimulated sample may serve as a baseline profile data set for the sample taken after stimulation. The baseline data set may also be derived from a library containing profile data sets of a population or set of subjects having some defining characteristic or biological condition. The baseline profile data set may also correspond to some ex vivo or in vitro properties associated with an in vitro cell culture. The resultant calibrated profile data sets may then be stored as a record in a database or library along with or separate from the baseline profile data base and optionally the first profile data set al. though the first profile data set would normally become incorporated into a baseline profile data set under suitable classification criteria. The remarkable consistency of Gene Expression Profiles associated with a given biological condition makes it valuable to store profile data, which can be used, among other things for normative reference purposes. The normative reference can serve to indicate the degree to which a subject conforms to a given biological condition (healthy or diseased) and, alternatively or in addition, to provide a target for clinical intervention.

Calibrated Data

Given the repeatability achieved in measurement of gene expression, described above in connection with “Gene Expression Panels” (Precision Profiles™) and “gene amplification”, it was concluded that where differences occur in measurement under such conditions, the differences are attributable to differences in biological condition. Thus, it has been found that calibrated profile data sets are highly reproducible in samples taken from the same individual under the same conditions. Similarly, it has been found that calibrated profile data sets are reproducible in samples that are repeatedly tested. Also found have been repeated instances wherein calibrated profile data sets obtained when samples from a subject are exposed ex vivo to a compound are comparable to calibrated profile data from a sample that has been exposed to a sample in vivo.

Calculation of Calibrated Profile Data Sets and Computational Aids

The calibrated profile data set may be expressed in a spreadsheet or represented graphically for example, in a bar chart or tabular form but may also be expressed in a three dimensional representation. The function relating the baseline and profile data may be a ratio expressed as a logarithm. The constituent may be itemized on the x-axis and the logarithmic scale may be on the y-axis. Members of a calibrated data set may be expressed as a positive value representing a relative enhancement of gene expression or as a negative value representing a relative reduction in gene expression with respect to the baseline.

Each member of the calibrated profile data set should be reproducible within a range with respect to similar samples taken from the subject under similar conditions. For example, the calibrated profile data sets may be reproducible within 20%, and typically within 10%. In accordance with embodiments of the invention, a pattern of increasing, decreasing and no change in relative gene expression from each of a plurality of gene loci examined in the Gene Expression Panel (Precision Profile™) may be used to prepare a calibrated profile set that is informative with regards to a biological condition, biological efficacy of an agent treatment conditions or for comparison to populations or sets of subjects or samples, or for comparison to populations of cells. Patterns of this nature may be used to identify likely candidates for a drug trial, used alone or in combination with other clinical indicators to be diagnostic or prognostic with respect to a biological condition or may be used to guide the development of a pharmaceutical or nutraceutical through manufacture, testing and marketing.

The numerical data obtained from quantitative gene expression and numerical data from calibrated gene expression relative to a baseline profile data set may be stored in databases or digital storage mediums and may be retrieved for purposes including managing patient health care or for conducting clinical trials or for characterizing a drug. The data may be transferred in physical or wireless networks via the World Wide Web, email, or internet access site for example or by hard copy so as to be collected and pooled from distant geographic sites.

The method also includes producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, and wherein the baseline profile data set is related to the colorectal cancer or conditions related to colorectal cancer to be evaluated, with the calibrated profile data set being a comparison between the first profile data set and the baseline profile data set, thereby providing evaluation of colorectal cancer or conditions related to colorectal cancer of the subject.

In yet other embodiments, the function is a mathematical function and is other than a simple difference, including a second function of the ratio of the corresponding member of first profile data set to the corresponding member of the baseline profile data set, or a logarithmic function. In such embodiments, the first sample is obtained and the first profile data set quantified at a first location, and the calibrated profile data set is produced using a network to access a database stored on a digital storage medium in a second location, wherein the database may be updated to reflect the first profile data set quantified from the sample. Additionally, using a network may include accessing a global computer network.

In an embodiment of the present invention, a descriptive record is stored in a single database or multiple databases where the stored data includes the raw gene expression data (first profile data set) prior to transformation by use of a baseline profile data set, as well as a record of the baseline profile data set used to generate the calibrated profile data set including for example, annotations regarding whether the baseline profile data set is derived from a particular Signature Panel and any other annotation that facilitates interpretation and use of the data.

Because the data is in a universal format, data handling may readily be done with a computer. The data is organized so as to provide an output optionally corresponding to a graphical representation of a calibrated data set.

The above described data storage on a computer may provide the information in a form that can be accessed by a user. Accordingly, the user may load the information onto a second access site including downloading the information. However, access may be restricted to users having a password or other security device so as to protect the medical records contained within. A feature of this embodiment of the invention is the ability of a user to add new or annotated records to the data set so the records become part of the biological information.

The graphical representation of calibrated profile data sets pertaining to a product such as a drug provides an opportunity for standardizing a product by means of the calibrated profile, more particularly a signature profile. The profile may be used as a feature with which to demonstrate relative efficacy, differences in mechanisms of actions, etc. compared to other drugs approved for similar or different uses.

The various embodiments of the invention may be also implemented as a computer program product for use with a computer system. The product may include program code for deriving a first profile data set and for producing calibrated profiles. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or transmittable to a computer system via a modem or other interface device, such as a communications adapter coupled to a network. The network coupling may be for example, over optical or wired communications lines or via wireless techniques (for example, microwave, infrared or other transmission techniques) or some combination of these. The series of computer instructions preferably embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (for example, shrink wrapped software), preloaded with a computer system (for example, on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a network (for example, the Internet or World Wide Web). In addition, a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.

The calibration profile data sets in graphical or tabular form, the associated databases, and the calculated index or derived algorithm, together with information extracted from the panels, the databases, the data sets or the indices or algorithms are commodities that can be sold together or separately for a variety of purposes as described in WO 01/25473.

In other embodiments, a clinical indicator may be used to assess the colorectal cancer or conditions related to colorectal cancer of the relevant set of subjects by interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator is selected from the group consisting of blood chemistry, X-ray or other radiological or metabolic imaging technique, molecular markers in the blood (e.g., carcinoembryonic antigen, CA19-9), other chemical assays, and physical findings.

Index Construction

In combination, (i) the remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population or set of subject or samples, or across a population of cells and (ii) the use of procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel (Precision Profile™) giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar, make possible the use of an index that characterizes a Gene Expression Profile, and which therefore provides a measurement of a biological condition.

An index may be constructed using an index function that maps values in a Gene Expression Profile into a single value that is pertinent to the biological condition at hand. The values in a Gene Expression Profile are the amounts of each constituent of the Gene Expression Panel (Precision Profile™). These constituent amounts form a profile data set, and the index function generates a single value—the index—from the members of the profile data set.

The index function may conveniently be constructed as a linear sum of terms, each term being what is referred to herein as a “contribution function” of a member of the profile data set. For example, the contribution function may be a constant times a power of a member of the profile data set. So the index function would have the form

I=ΣciMi ^(P(i)),

where I is the index, Mi is the value of the member i of the profile data set, Ci is a constant, and P(i) is a power to which Mi is raised, the sum being formed for all integral values of i up to the number of members in the data set. We thus have a linear polynomial expression. The role of the coefficient Ci for a particular gene expression specifies whether a higher ΔCt value for this gene either increases (a positive Ci) or decreases (a lower value) the likelihood of colorectal cancer, the ΔCt values of all other genes in the expression being held constant.

The values Ci and P(i) may be determined in a number of ways, so that the index I is informative of the pertinent biological condition. One way is to apply statistical techniques, such as latent class modeling, to the profile data sets to correlate clinical data or experimentally derived data, or other data pertinent to the biological condition. In this connection, for example, may be employed the software from Statistical Innovations, Belmont, Mass., called Latent Gold®. Alternatively, other simpler modeling techniques may be employed in a manner known in the art. The index function for colorectal cancer may be constructed, for example, in a manner that a greater degree of colorectal cancer (as determined by the profile data set for the any of the Precision Profiles™ (listed in Tables 1-5) described herein) correlates with a large value of the index function.

Just as a baseline profile data set, discussed above, can be used to provide an appropriate normative reference, and can even be used to create a Calibrated profile data set, as discussed above, based on the normative reference, an index that characterizes a Gene Expression Profile can also be provided with a normative value of the index function used to create the index. This normative value can be determined with respect to a relevant population or set of subjects or samples or to a relevant population of cells, so that the index may be interpreted in relation to the normative value. The relevant population or set of subjects or samples, or relevant population of cells may have in common a property that is at least one of age range, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure.

As an example, the index can be constructed, in relation to a normative Gene Expression Profile for a population or set of healthy subjects, in such a way that a reading of approximately 1 characterizes normative Gene Expression Profiles of healthy subjects. Let us further assume that the biological condition that is the subject of the index is colorectal cancer; a reading of 1 in this example thus corresponds to a Gene Expression Profile that matches the norm for healthy subjects. A substantially higher reading then may identify a subject experiencing colorectal cancer, or a condition related to colorectal cancer. The use of 1 as identifying a normative value, however, is only one possible choice; another logical choice is to use 0 as identifying the normative value. With this choice, deviations in the index from zero can be indicated in standard deviation units (so that values lying between −1 and +1 encompass 90% of a normally distributed reference population or set of subjects. Since it was determined that Gene Expression Profile values (and accordingly constructed indices based on them) tend to be normally distributed, the 0-centered index constructed in this manner is highly informative. It therefore facilitates use of the index in diagnosis of disease and setting objectives for treatment.

Still another embodiment is a method of providing an index pertinent to colorectal cancer or conditions related to colorectal cancer of a subject based on a first sample from the subject, the first sample providing a source of RNAs, the method comprising deriving from the first sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA constituent in a panel of constituents selected so that measurement of the constituents is indicative of the presumptive signs of colorectal cancer, the panel including at least one of any of the genes listed in the Precision Profiles™ (listed in Tables 1-5). In deriving the profile data set, such measure for each constituent is achieved under measurement conditions that are substantially repeatable, at least one measure from the profile data set is applied to an index function that provides a mapping from at least one measure of the profile data set into one measure of the presumptive signs of colorectal cancer, so as to produce an index pertinent to the colorectal cancer or conditions related to colorectal cancer of the subject.

As another embodiment of the invention, an index function I of the form

I=C ₀ ΣC _(i) M _(Ii) ^(P1(i)) M _(2i) ^(P2(i)),

can be employed, where M₁ and M₂ are values of the member i of the profile data set, C_(i) is a constant determined without reference to the profile data set, and P1 and P2 are powers to which M₁ and M₂ are raised. The role of P1(i) and P2(i) is to specify the specific functional form of the quadratic expression, whether in fact the equation is linear, quadratic, contains cross-product terms, or is constant. For example, when P1=P2=0, the index function is simply the sum of constants; when P1=1 and P2=0, the index function is a linear expression; when P1=P2=1, the index function is a quadratic expression.

The constant C₀ serves to calibrate this expression to the biological population of interest that is characterized by having colorectal cancer. In this embodiment, when the index value equals 0, the odds are 50:50 of the subject having colorectal cancer vs a normal subject. More generally, the predicted odds of the subject having colorectal cancer is [exp(I_(i))], and therefore the predicted probability of having colorectal cancer is [exp(I_(i))]/[1+exp((I_(i))]. Thus, when the index exceeds 0, the predicted probability that a subject has colorectal cancer is higher than 0.5, and when it falls below 0, the predicted probability is less than 0.5.

The value of C₀ may be adjusted to reflect the prior probability of being in this population based on known exogenous risk factors for the subject. In an embodiment where C₀ is adjusted as a function of the subject's risk factors, where the subject has prior probability p_(i) of having colorectal cancer based on such risk factors, the adjustment is made by increasing (decreasing) the unadjusted C₀ value by adding to C₀ the natural logarithm of the following ratio: the prior odds of having colorectal cancer taking into account the risk factors/the overall prior odds of having colorectal cancer without taking into account the risk factors.

Performance and Accuracy Measures of the Invention

The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, the invention is intended to provide accuracy in clinical diagnosis and prognosis. The accuracy to of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects having colorectal cancer is based on whether the subjects have an “effective amount” or a “significant alteration” in the levels of a cancer associated gene. By “effective amount” or “significant alteration”, it is meant that the measurement of an appropriate number of cancer associated gene (which may be one or more) is different than the predetermined cut-off point (or threshold value) for that cancer associated gene and therefore indicates that the subject has colorectal cancer for which the cancer associated gene(s) is a determinant.

The difference in the level of cancer associated gene(s) between normal and abnormal is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical and clinical accuracy, generally but not always requires that combinations of several cancer associated gene(s) be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant cancer associated gene index.

In the categorical diagnosis of a disease state, changing the cut point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject's condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. Use of statistics such as AUC, encompassing all potential cut point values, is preferred for most categorical risk measures using the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred.

Using such statistics, an “acceptable degree of diagnostic accuracy”, is herein defined as a test or assay (such as the test of the invention for determining an effective amount or a significant alteration of cancer associated gene(s), which thereby indicates the presence of a colorectal cancer in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

By a “very high degree of diagnostic accuracy”, it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, desirably at least 0.775, more desirably at least 0.800, preferably at least 0.825, more preferably at least 0.850, and most preferably at least 0.875.

The predictive value of any test depends on the sensitivity and specificity of the test, and on the prevalence of the condition in the population being tested. This notion, based on Bayes' theorem, provides that the greater the likelihood that the condition being screened for is present in an individual or in the population (pre-test probability), the greater the validity of a positive test and the greater the likelihood that the result is a true positive. Thus, the problem with using a test in any population where there is a low likelihood of the condition being present is that a positive result has limited value (i.e., more likely to be a false positive). Similarly, in populations at very high risk, a negative test result is more likely to be a false negative.

As a result, ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon). Alternatively, absolute risk and relative risk ratios as defined elsewhere in this disclosure can be employed to determine the degree of clinical utility. Populations of subjects to be tested can also be categorized into quartiles by the test's measurement values, where the top quartile (25% of the population) comprises the group of subjects with the highest relative risk for developing colorectal cancer, and the bottom quartile comprising the group of subjects having the lowest relative risk for developing colorectal cancer. Generally, values derived from tests or assays having over 2.5 times the relative risk from top to bottom quartile in a low prevalence population are considered to have a “high degree of diagnostic accuracy,” and those with five to seven times the relative risk for each quartile are considered to have a “very high degree of diagnostic accuracy.” Nonetheless, values derived from tests or assays having only 1.2 to 2.5 times the relative risk for each quartile remain clinically useful are widely used as risk factors for a disease. Often such lower diagnostic accuracy tests must be combined with additional parameters in order to derive meaningful clinical thresholds for therapeutic intervention, as is done with the aforementioned global risk assessment indices.

A health economic utility function is yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each. Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects. As a performance measure, it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.

In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category or risk category (such as those at risk for having a bone fracture) has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer-Lemeshow P-value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% CI) based on a historical observed cohort's predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City, Calif.).

In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the cancer associated gene(s) of the invention allows for one of skill in the art to use the cancer associated gene(s) to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.

Results from the cancer associated gene(s) indices thus derived can then be validated through their calibration with actual results, that is, by comparing the predicted versus observed rate of disease in a given population, and the best predictive cancer associated gene(s) selected for and optimized through mathematical models of increased complexity. Many such formula may be used; beyond the simple non-linear transformations, such as logistic regression, of particular interest in this use of the present invention are structural and synactic classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as the Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, as well as other formula described herein.

Furthermore, the application of such techniques to panels of multiple cancer associated gene(s) is provided, as is the use of such combination to create single numerical “risk indices” or “risk scores” encompassing information from multiple cancer associated gene(s) inputs. Individual B cancer associated gene(s) may also be included or excluded in the panel of cancer associated gene(s) used in the calculation of the cancer associated gene(s) indices so derived above, based on various measures of relative performance and calibration in validation, and employing through repetitive training methods such as forward, reverse, and stepwise selection, as well as with genetic algorithm approaches, with or without the use of constraints on the complexity of the resulting cancer associated gene(s) indices.

The above measurements of diagnostic accuracy for cancer associated gene(s) are only a few of the possible measurements of the clinical performance of the invention. It should be noted that the appropriateness of one measurement of clinical accuracy or another will vary based upon the clinical application, the population tested, and the clinical consequences of any potential misclassification of subjects. Other important aspects of the clinical and overall performance of the invention include the selection of cancer associated gene(s) so as to reduce overall cancer associated gene(s) variability (whether due to method (analytical) or biological (pre-analytical variability, for example, as in diurnal variation), or to the integration and analysis of results (post-analytical variability) into indices and cut-off ranges), to assess analyte stability or sample integrity, or to allow the use of differing sample matrices amongst blood, cells, serum, plasma, urine, etc.

Kits

The invention also includes a colorectal cancer detection reagent, i.e., nucleic acids that specifically identify one or more colorectal cancer or condition related to colorectal cancer nucleic acids (e.g., any gene listed in Tables 1-5, oncogenes, tumor suppression genes, tumor progression genes, angiogenesis genes and lymphogenesis genes; sometimes referred to herein as colorectal cancer associated genes or colorectal cancer associated constituents) by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the colorectal cancer genes nucleic acids or antibodies to proteins encoded by the colorectal cancer gene nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the colorectal cancer genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain in separate containers a nucleic acid or antibody (either already bound to a solid matrix or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label. Instructions (i.e., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of PCR, a Northern hybridization or a sandwich ELISA, as known in the art.

For example, colorectal cancer gene detection reagents can be immobilized on a solid matrix such as a porous strip to form at least one colorectal cancer gene detection site. The measurement or detection region of the porous strip may include a plurality of sites containing a nucleic acid. A test strip may also contain sites for negative and/or positive controls. Alternatively, control sites can be located on a separate strip from the test strip. Optionally, the different detection sites may contain different amounts of immobilized nucleic acids, i.e., a higher amount in the first detection site and lesser amounts in subsequent sites. Upon the addition of test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of colorectal cancer genes present in the sample. The detection sites may be configured in any suitably detectable shape and are typically in the shape of a bar or dot spanning the width of a test strip.

Alternatively, colorectal cancer detection genes can be labeled (e.g., with one or more fluorescent dyes) and immobilized on lyophilized beads to form at least one colorectal cancer gene detection site. The beads may also contain sites for negative and/or positive controls. Upon addition of the test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of colorectal cancer genes present in the sample.

Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by colorectal cancer genes (see Tables 1-5). In various embodiments, the expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 40 or 50 or more of the sequences represented by colorectal cancer genes (see Tables 1-5) can be identified by virtue of binding to the array. The substrate array can be on, i.e., a solid substrate, i.e., a “chip” as described in U.S. Pat. No. 5,744,305. Alternatively, the substrate array can be a solution array, i.e., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic.

The skilled artisan can routinely make antibodies, nucleic acid probes, i.e., oligonucleotides, aptamers, siRNAs, antisense oligonucleotides, against any of the colorectal cancer genes listed in Tables 1-5.

Other Embodiments

While the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

EXAMPLES Example 1 Patient Population

RNA was isolated using the PAXgene System from blood samples obtained from a total of 23 subjects suffering from colon cancer and 50 healthy, normal (i.e., not suffering from or diagnosed with colon cancer) subjects. These RNA samples were used for the gene expression analysis studies described in Examples 3-7 below.

The inclusion criteria for the colon cancer subjects that participated in the study were as follows: each of the subjects had defined, newly diagnosed disease, the blood samples were obtained prior to initiation of any treatment for colon cancer, and each subject in the study was 18 years or older, and able to provide consent.

The following criteria were used to exclude subjects from the study: any treatment with immunosuppressive drugs, corticosteroids or investigational drugs; diagnosis of acute and chronic infectious diseases (renal or chest infections, previous TB, HIV infection or AIDS, or active cytomegalovirus); symptoms of severe progression or uncontrolled renal, hepatic, hematological, gastrointestinal, endocrine, pulmonary, neurological, or cerebral disease; and pregnancy.

Example 2 Enumeration and Classification Methodology Based on Logistic Regression Models Introduction

The following methods were used to generate 1, 2, and 3-gene models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects, with at least 75% classification accuracy, as described in Examples 3-7 below.

Given measurements on G genes from samples of N₁ subjects belonging to group 1 and N₂ members of group 2, the purpose was to identify models containing g<G genes which discriminate between the 2 groups. The groups might be such that one consists of reference subjects (e.g., healthy, normal subjects) while the other group might have a specific disease, or subjects in group 1 may have disease A while those in group 2 may have disease B.

Specifically, parameters from a linear logistic regression model were estimated to predict a subject's probability of belonging to group 1 given his (her) measurements on the g genes in the model. After all the models were estimated (all G 1-gene models were estimated, as well as

${{{all}\begin{pmatrix} G \\ 2 \end{pmatrix}} = {G*{\left( {G\text{-}1} \right)/2}\mspace{14mu} 2\text{-}{gene}\mspace{14mu} {models}}},$

and all (G 3)=G*(G−1)*(G−2)/6 3-gene models based on G genes (number of combinations taken 3 at a time from G)), they were evaluated using a 2-dimensional screening process. The first dimension employed a statistical screen (significance of incremental p-values) that eliminated models that were likely to overfit the data and thus may not validate when applied to new subjects. The second dimension employed a clinical screen to eliminate models for which the expected misclassification rate was higher than an acceptable level. As a threshold analysis, the gene models showing less than 75% discrimination between N₁ subjects belonging to group 1 and N₂ members of group 2 (i.e., misclassification of 25% or more of subjects in either of the 2 sample groups), and genes with incremental p-values that were not statistically significant, were eliminated.

Methodological, Statistical and Computing Tools Used

The Latent GOLD program (Vermunt and Magidson, 2005) was used to estimate the logistic regression models. For efficiency in processing the models, the LG-Syntax™ Module available with version 4.5 of the program (Vermunt and Magidson, 2007) was used in batch mode, and all g-gene models associated with a particular dataset were submitted in a single run to be estimated. That is, all 1-gene models were submitted in a single run, all 2-gene models were submitted in a second run, etc.

The Data

The data consists of ΔC_(T) values for each sample subject in each of the 2 groups (e.g., to cancer subject vs. reference (e.g., healthy, normal subjects) on each of G(k) genes obtained from a particular class k of genes. For a given disease, separate analyses were performed based on disease specific genes, including without limitation genes specific for prostate, breast, ovarian, cervical, lung, colon, and skin cancer, (k=1), inflammatory genes (k=2), human cancer general genes (k=3), genes from a cross cancer gene panel (k=4), and genes in the EGR family (k=5).

Analysis Steps

The steps in a given analysis of the G(k) genes measured on N₁ subjects in group 1 and N₂ subjects in group 2 are as follows:

-   1) Eliminate low expressing genes: In some instances, target gene     FAM measurements were beyond the detection limit (i.e., very high     ΔC_(T) values which indicate low expression) of the particular     platform instrument used to detect and quantify constituents of a     Gene Expression Panel (Precision Profile™). To address the issue of     “undetermined” gene expression measures as lack of expression for a     particular gene, the detection limit was reset and the     “undetermined” constituents were “flagged”, as previously described.     C_(T) normalization (Δ C_(T)) and relative expression calculations     that have used re-set FAM C_(T) values were also flagged. In some     instances, these low expressing genes (i.e., re-set FAM C_(T)     values) were eliminated from the analysis in step 1 if 50% or more     ΔC_(T) values from either of the 2 groups were flagged. Although     such genes were eliminated from the statistical analyses described     herein, one skilled in the art would recognize that such genes may     be relevant in a disease state. -   2) Estimate logistic regression (logit) models predicting P(i)=the     probability of being in group 1 for each subject i=1, 2, . . . ,     N₁+N₂. Since there are only 2 groups, the probability of being in     group 2 equals 1−P(i). The maximum likelihood (ML) algorithm     implemented in Latent GOLD 4.0 (Vermunt and Magidson, 2005) was used     to estimate the model parameters. All 1-gene models were estimated     first, followed by all 2-gene models and in cases where the sample     sizes N₁ and N₂ were sufficiently large, all 3-gene models were     estimated. -   3) Screen out models that fail to meet the statistical or clinical     criteria: Regarding the statistical criteria, models were retained     if the incremental p-values for the parameter estimates for each     gene (i.e., for each predictor in the model) fell below the cutoff     point alpha=0.05. Regarding the clinical criteria, models were     retained if the percentage of cases within each group (e.g., disease     group, and reference group (e.g., healthy, normal subjects) that was     correctly predicted to be in that group was at least 75%. For     technical details, see the section “Application of the Statistical     and Clinical Criteria to Screen Models”. -   4) Each model yielded an index that could be used to rank the sample     subjects. Such an index value could also be computed for new cases     not included in the sample. See the section “Computing Model-based     Indices for each Subject” for details on how this index was     calculated. -   5) A cutoff value somewhere between the lowest and highest index     value was selected and based on this cutoff, subjects with indices     above the cutoff were classified (predicted to be) in the disease     group, those below the cutoff were classified into the reference     group (i.e., normal, healthy subjects). Based on such     classifications, the percent of each group that is correctly     classified was determined. See the section labeled “Classifying     Subjects into Groups” for details on how the cutoff was chosen. -   6) Among all models that survived the screening criteria (Step 3),     an entropy-based R² statistic was used to rank the models from high     to low, i.e., the models with the highest percent classification     rate to the lowest percent classification rate. The top 5 such     models are then evaluated with respect to the percent correctly     classified and the one having the highest percentages was selected     as the single “best” model. A discrimination plot was provided for     the best model having an 85% or greater percent classification rate.     For details on how this plot was developed, see the section     “Discrimination Plots” below.

While there are several possible R² statistics that might be used for this purpose, it was determined that the one based on entropy was most sensitive to the extent to which a model yields clear separation between the 2 groups. Such sensitivity provides a model which can be used as a tool by a practitioner (e.g., primary care physician, oncologist, etc.) to ascertain the necessity of future screening or treatment options. For more detail on this issue, see the section labeled “Using R² Statistics to Rank Models” below.

Computing Model-Based Indices for Each Subject

The model parameter estimates were used to compute a numeric value (logit, odds or probability) for each diseased and reference subject (e.g., healthy, normal subject) in the sample. For illustrative purposes only, in an example of a 2-gene logit model for cancer containing the genes ALOX5 and S100A6, the following parameter estimates listed in Table A were obtained:

TABLE A Cancer alpha(1) 18.37 Normals alpha(2) −18.37 Predictors ALOX5 beta(1) −4.81 S100A6 beta(2) 27.9 For a given subject with particular ΔC_(T) values observed for these genes, the predicted logit associated with cancer vs. reference (i.e., normals) was computed as:

LOGIT(ALOX5,S100A6)=[alpha(1)−alpha(2)]+beta(1)*ALOX5+beta(2)*S100A6.

The predicted odds of having cancer would be:

ODDS(ALOX5,S100A6)=exp[LOGIT(ALOX5,S100A6)]

and the predicted probability of belonging to the cancer group is:

P(ALOX5,S100A6)=ODDS(ALOX5,S100A6)/[1+ODDS(ALOX5,S100A6)]

Note that the ML estimates for the alpha parameters were based on the relative proportion of the group sample sizes. Prior to computing the predicted probabilities, the alpha estimates may be adjusted to take into account the relative proportion in the population to which the model will be applied (for example, without limitation, the incidence of prostate cancer in the population of adult men in the U.S., the incidence of breast cancer in the population of adult women in the U.S., etc.)

Classifying Subjects into Groups

The “modal classification rule” was used to predict into which group a given case belongs. This rule classifies a case into the group for which the model yields the highest predicted probability. Using the same cancer example previously described (for illustrative purposes only), use of the modal classification rule would classify any subject having P>0.5 into the cancer group, the others into the reference group (e.g., healthy, normal subjects). The percentage of all N₁ cancer subjects that were correctly classified were computed as the number of such subjects having P>0.5 divided by N₁. Similarly, the percentage of all N₂ reference (e.g., normal healthy) subjects that were correctly classified were computed as the number of such subjects having P≦0.5 divided by N₂. Alternatively, a cutoff point P₀ could be used instead of the modal classification rule so that any subject i having P(i)>P₀ is assigned to the cancer group, and otherwise to the Reference group (e.g., normal, healthy group).

Application of the Statistical and Clinical Criteria to Screen Models Clinical Screening Criteria

In order to determine whether a model met the clinical 75% correct classification criteria, the following approach was used:

-   -   A. All sample subjects were ranked from high to low by their         predicted probability P (e.g., see Table B).     -   B. Taking P₀(i)=P(i) for each subject, one at a time, the         percentage of group 1 and group 2 that would be correctly         classified, P₁(i) and P₂(i) was computed.     -   C. The information in the resulting table was scanned and any         models for which none of the potential cutoff probabilities met         the clinical criteria (i.e., no cutoffs P₀(i) exist such that         both P₁(i)>0.75 and P₂(i)>0.75) were eliminated. Hence, models         that did not meet the clinical criteria were eliminated.

The example shown in Table B has many cut-offs that meet this criteria. For example, the cutoff P₀=0.4 yields correct classification rates of 92% for the reference group (i.e., normal, healthy subjects), and 93% for Cancer subjects. A plot based on this cutoff is shown in FIG. 1 and described in the section “Discrimination Plots”.

Statistical Screening Criteria

In order to determine whether a model met the statistical criteria, the following approach was used to compute the incremental p-value for each gene g=1, 2, . . . , G as follows:

-   -   i. Let LSQ(0) denote the overall model L-squared output by         Latent GOLD for an unrestricted model.     -   ii. Let LSQ(g) denote the overall model L-squared output by         Latent GOLD for the restricted version of the model where the         effect of gene g is restricted to 0.     -   iii. With 1 degree of freedom, use a ‘components of chi-square’         table to determine the p-value associated with the LR difference         statistic LSQ(g)-LSQ(0).         Note that this approach required estimating g restricted models         as well as 1 unrestricted model.

Discrimination Plots

For a 2-gene model, a discrimination plot consisted of plotting the ΔC_(T) values for each subject in a scatterplot where the values associated with one of the genes served as the vertical axis, the other serving as the horizontal axis. Two different symbols were used for the points to denote whether the subject belongs to group 1 or 2.

A line was appended to a discrimination graph to illustrate how well the 2-gene model discriminated between the 2 groups. The slope of the line was determined by computing the ratio of the ML parameter estimate associated with the gene plotted along the horizontal axis divided by the corresponding estimate associated with the gene plotted along the vertical axis. The intercept of the line was determined as a function of the cutoff point. For the cancer example model based on the 2 genes ALOX5 and S100A6 shown in FIG. 1, the equation for the line associated with the cutoff of 0.4 is ALOX5=7.7+0.58*S100A6. This line provides correct classification rates of 93% and 92% (4 of 57 cancer subjects misclassified and only 4 of 50 reference (i.e., normal) subjects misclassified).

For a 3-gene model, a 2-dimensional slice defined as a linear combination of 2 of the genes was plotted along one of the axes, the remaining gene being plotted along the other axis. The particular linear combination was determined based on the parameter estimates. For example, if a 3^(rd) gene were added to the 2-gene model consisting of ALOX5 and S100A6 and the parameter estimates for ALOX5 and S100A6 were beta(1) and beta(2) respectively, the linear combination beta(1)*ALOX5+beta(2)*S100A6 could be used. This approach can be readily extended to the situation with 4 or more genes in the model by taking additional linear combinations. For example, with 4 genes one might use beta(1)*ALOX5+beta(2)*S100A6-along one axis and beta(3)*gene3+beta(4)*gene4 along the other, or beta(1)*ALOX530 beta(2)*S100A6+beta(3)*gene3 along one axis and gene4 along the other axis. When producing such plots with 3 or more genes, genes with parameter estimates having the same sign were chosen for combination.

Using R²Statistics to Rank Models

The R² in traditional OLS (ordinary least squares) linear regression of a continuous dependent variable can be interpreted in several different ways, such as 1) proportion of variance accounted for, 2) the squared correlation between the observed and predicted values, and 3) a transformation of the F-statistic. When the dependent variable is not continuous but categorical (in our models the dependent variable is dichotomous—membership in the diseased group or reference group), this standard R² defined in terms of variance (see definition 1 above) is only one of several possible measures. The term ‘pseudo R²’ has been coined for the generalization of the standard variance-based R² for use with categorical dependent variables, as well as other settings where the usual assumptions that justify OLS do not apply.

The general definition of the (pseudo) R² for an estimated model is the reduction of errors compared to the errors of a baseline model. For the purpose of the present invention, the estimated model is a logistic regression model for predicting group membership based on 1 or more continuous predictors (ΔC_(T) measurements of different genes). The baseline model is the regression model that contains no predictors; that is, a model where the regression coefficients are restricted to 0. More precisely, the pseudo R² is defined as:

R ²=[Error(baseline)−Error(model)]/Error(baseline)

Regardless how error is defined, if prediction is perfect, Error(model)=0 which yields R²=1. Similarly, if all of the regression coefficients do in fact turn out to equal 0, the model is equivalent to the baseline, and thus R²=0. In general, this pseudo R² falls somewhere between 0 and 1.

When Error is defined in terms of variance, the pseudo R² becomes the standard R². When the dependent variable is dichotomous group membership, scores of 1 and 0, −1 and +1, or any other 2 numbers for the 2 categories yields the same value for R². For example, if the dichotomous dependent variable takes on the scores of 1 and 0, the variance is defined as P*(1−P) where P is the probability of being in 1 group and 1−P the probability of being in the other.

A common alternative in the case of a dichotomous dependent variable, is to define error in terms of entropy. In this situation, entropy can be defined as P*1n(P)*(1−P)*ln(1−P) (for further discussion of the variance and the entropy based R², see Magidson, Jay, “Qualitative Variance, Entropy and Correlation Ratios for Nominal Dependent Variables,” Social Science Research 10 (June), pp. 177-194).

The R² statistic was used in the enumeration methods described herein to identify the “best” gene-model. R² can be calculated in different ways depending upon how the error variation and total observed variation are defined. For example, four different R² measures output by Latent GOLD are based on:

a) Standard variance and mean squared error (MSE) b) Entropy and minus mean log-likelihood (−MLL) c) Absolute variation and mean absolute error (MAE) d) Prediction errors and the proportion of errors under modal assignment (PPE)

Each of these 4 measures equal 0 when the predictors provide zero discrimination between the groups, and equal 1 if the model is able to classify each subject into their actual group with 0 error. For each measure, Latent GOLD defines the total variation as the error of the baseline (intercept-only) model which restricts the effects of all predictors to 0. Then for each, R² is defined as the proportional reduction of errors in the estimated model compared to the baseline model. For the 2-gene cancer example used to illustrate the enumeration methodology described herein, the baseline model classifies all cases as being in the diseased group since this group has a larger sample size, resulting in 50 misclassifications (all 50 normal subjects are misclassified) for a prediction error of 50/107=0.467. In contrast, there are only 10 prediction errors (=10/107=0.093) based on the 2-gene model using the modal assignment rule, thus yielding a prediction error R² of 1−0.093/0.467=0.8. As shown in Exhibit 1, 4 normal and 6 cancer subjects would be misclassified-using the modal assignment rule. Note that the modal rule utilizes P₀=0.5 as the cutoff. If P₀=0.4 were used instead, there would be only 8 misclassified subjects.

The sample discrimination plot shown in FIG. 1 is for a 2-gene model for cancer based on disease-specific genes. The 2 genes in the model are ALOX5 and S100A6 and only 8 subjects are misclassified (4 blue circles corresponding to normal subjects fall to the right and below the line, while 4 red Xs corresponding to misclassified cancer subjects lie above the line).

To reduce the likelihood of obtaining models that capitalize on chance variations in the observed samples the models may be limited to contain only M genes as predictors in the model. (Although a model may meet the significance criteria, it may overfit data and thus would not be expected to validate when applied to a new sample of subjects.) For example, for M=2, all models would be estimated which contain:

A. 1-gene G such models

B.

${2\text{-}{gene}\mspace{14mu} {models}\text{-}\begin{pmatrix} G \\ 2 \end{pmatrix}} = {G*{\left( {G\text{-}1} \right)/2}\mspace{14mu} {such}\mspace{14mu} {models}}$

C. 3-gene models—(G 3)=G*(G−1)*(G−2)/6 such models

Computation of the Z-Statistic

The Z-Statistic associated with the test of significance between the mean ΔC_(T) values for the cancer and normal groups for any gene g was calculated as follows:

i. Let LL[g] denote the log of the likelihood function that is maximized under the logistic regression model that predicts group membership (Cancer vs. Normal) as a function of the ΔC_(T) value associated with gene g. There are 2 parameters in this model—an intercept and a slope. ii. Let LL(0) denote the overall model L-squared output by Latent GOLD for the restricted version of the model where the slope parameter reflecting the effect of gene g is restricted to 0. This model has only 1 unrestricted parameter—the intercept. iii. With 2-1=1 degree of freedom (the difference in the number of unrestricted parameters in the models), one can use a ‘components of chi-square’ table to determine the p-value associated with the Log Likelihood difference statistic LLDiff=−2*(LL[0]−LL[g])=2*(LL[g]−LL[0]). iv. Since the chi-squared statistic with 1 df is the square of a Z-statistic, the magnitude of the Z-statistic can be computed as the square root of the LLDiff. The sign of Z is negative if the mean ΔC_(T) value for the cancer group on gene g is less than the corresponding mean for the normal group, and positive if it is greater. v. These Z-statistics can be plotted as a bar graph. The length of the bar has a monotonic relationship with the p-value.

TABLE B ΔC_(T) Values and Model Predicted Probability of Cancer for Each Subject ALOX5 S100A6 P Group 13.92 16.13 1.0000 Cancer 13.90 15.77 1.0000 Cancer 13.75 15.17 1.0000 Cancer 13.62 14.51 1.0000 Cancer 15.33 17.16 1.0000 Cancer 13.86 14.61 1.0000 Cancer 14.14 15.09 1.0000 Cancer 13.49 13.60 0.9999 Cancer 15.24 16.61 0.9999 Cancer 14.03 14.45 0.9999 Cancer 14.98 16.05 0.9999 Cancer 13.95 14.25 0.9999 Cancer 14.09 14.13 0.9998 Cancer 15.01 15.69 0.9997 Cancer 14.13 14.15 0.9997 Cancer 14.37 14.43 0.9996 Cancer 14.14 13.88 0.9994 Cancer 14.33 14.17 0.9993 Cancer 14.97 15.06 0.9988 Cancer 14.59 14.30 0.9984 Cancer 14.45 13.93 0.9978 Cancer 14.40 13.77 0.9972 Cancer 14.72 14.31 0.9971 Cancer 14.81 14.38 0.9963 Cancer 14.54 13.91 0.9963 Cancer 14.88 14.48 0.9962 Cancer 14.85 14.42 0.9959 Cancer 15.40 15.30 0.9951 Cancer 15.58 15.60 0.9951 Cancer 14.82 14.28 0.9950 Cancer 14.78 14.06 0.9924 Cancer 14.68 13.88 0.9922 Cancer 14.54 13.64 0.9922 Cancer 15.86 15.91 0.9920 Cancer 15.71 15.60 0.9908 Cancer 16.24 16.36 0.9858 Cancer 16.09 15.94 0.9774 Cancer 15.26 14.41 0.9705 Cancer 14.93 13.81 0.9693 Cancer 15.44 14.67 0.9670 Cancer 15.69 15.08 0.9663 Cancer 15.40 14.54 0.9615 Cancer 15.80 15.21 0.9586 Cancer 15.98 15.43 0.9485 Cancer 15.20 14.08 0.9461 Normal 15.03 13.62 0.9196 Cancer 15.20 13.91 0.9184 Cancer 15.04 13.54 0.8972 Cancer 15.30 13.92 0.8774 Cancer 15.80 14.68 0.8404 Cancer 15.61 14.23 0.7939 Normal 15.89 14.64 0.7577 Normal 15.44 13.66 0.6445 Cancer 16.52 15.38 0.5343 Cancer 15.54 13.67 0.5255 Normal 15.28 13.11 0.4537 Cancer 15.96 14.23 0.4207 Cancer 15.96 14.20 0.3928 Normal 16.25 14.69 0.3887 Cancer 16.04 14.32 0.3874 Cancer 16.26 14.71 0.3863 Normal 15.97 14.18 0.3710 Cancer 15.93 14.06 0.3407 Normal 16.23 14.41 0.2378 Cancer 16.02 13.91 0.1743 Normal 15.99 13.78 0.1501 Normal 16.74 15.05 0.1389 Normal 16.66 14.90 0.1349 Normal 16.91 15.20 0.0994 Normal 16.47 14.31 0.0721 Normal 16.63 14.57 0.0672 Normal 16.25 13.90 0.0663 Normal 16.82 14.84 0.0596 Normal 16.75 14.73 0.0587 Normal 16.69 14.54 0.0474 Normal 17.13 15.25 0.0416 Normal 16.87 14.72 0.0329 Normal 16.35 13.76 0.0285 Normal 16.41 13.83 0.0255 Normal 16.68 14.20 0.0205 Normal 16.58 13.97 0.0169 Normal 16.66 14.09 0.0167 Normal 16.92 14.49 0.0140 Normal 16.93 14.51 0.0139 Normal 17.27 15.04 0.0123 Normal 16.45 13.60 0.0116 Normal 17.52 15.44 0.0110 Normal 17.12 14.46 0.0051 Normal 17.13 14.46 0.0048 Normal 16.78 13.86 0.0047 Normal 17.10 14.36 0.0041 Normal 16.75 13.69 0.0034 Normal 17.27 14.49 0.0027 Normal 17.07 14.08 0.0022 Normal 17.16 14.08 0.0014 Normal 17.50 14.41 0.0007 Normal 17.50 14.18 0.0004 Normal 17.45 14.02 0.0003 Normal 17.53 13.90 0.0001 Normal 18.21 15.06 0.0001 Normal 17.99 14.63 0.0001 Normal 17.73 14.05 0.0001 Normal 17.97 14.40 0.0001 Normal 17.98 14.35 0.0001 Normal 18.47 15.16 0.0001 Normal 18.28 14.59 0.0000 Normal 18.37 14.71 0.0000 Normal

Example 3 Precision Profile™ for Colorectal Cancer

Custom primers and probes were prepared for the targeted 70 genes shown in the Precision Profile™ for Colorectal Cancer (shown in Table 1), selected to be informative relative to biological state of colon cancer patients. Gene expression profiles for the 70 colon cancer specific genes were analyzed using the 19 of the RNA samples obtained from colon cancer subjects, and the 50 RNA samples obtained from healthy, normal subjects, as described in Example 1.

Logistic regression models yielding the best discrimination between subjects diagnosed with colon cancer and normal subjects were generated using the enumeration and classification methodology described in Example 2. A listing of all 1 and 2-gene logistic regression models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects with at least 75% accuracy is shown in Table 1A, (read from left to right).

As shown in Table 1A, the 1 and 2-gene models are identified in the first two columns on the left side of Table 1A, ranked by their entropy R² value (shown in column 3, ranked from high to low). The number of subjects correctly classified or misclassified by each 1 or 2-gene model for each patient group (i.e., normal vs. colon cancer) is shown in columns 4-7. The percent normal subjects and percent colon cancer subjects correctly classified by the corresponding gene model is shown in columns 8 and 9. The incremental p-value for each first and second gene in the 1 or 2-gene model is shown in columns 10 and 11 (note p-values smaller than 1×10⁻¹⁷ are 20, reported as ‘0 ’). The total number of RNA samples analyzed in each patient group (i.e., normals vs. colon cancer), after exclusion of missing values, is shown in columns 12 and 13. The values missing from the total sample number for normal and/or colon cancer subjects shown in columns 12 and 13 correspond to instances in which values were excluded from the logistic regression analysis due to reagent limitations and/or instances where replicates did not meet quality metrics.

For example, the “best” logistic regression model (defined as the model with the highest entropy R² value, as described in Example 2) based on the 70 genes included in the Precision Profile™ for Colorectal Cancer is shown in the first row of Table 1A, read left to right. The first row of Table 1A lists a 2-gene model, MSH6 and PSEN2, capable of classifying normal subjects with 87.5% accuracy, and colon cancer subjects with 84.2% accuracy. A total number of 48 normal and 19 colon cancer RNA samples were analyzed for this 2-gene model, after exclusion of missing values. As shown in Table 1A, this 2-gene model correctly classifies 42 of the normal subjects as being in the normal patient population, and misclassifies 6 of the normal subjects as being in the colon cancer patient population. This 2-gene model correctly classifies 16 of the colon cancer subjects as being in the colon cancer patient population, and misclassifies 3 of the colon cancer subjects as being in the normal patient population. The p-value for the 1^(st) gene, MSH6 is 6.6E-11, the incremental p-value for the second gene, PSEN2, is 1.2E-06.

A discrimination plot of the 2-gene model, MSH6 and PSEN2, is shown in FIG. 2. As shown in FIG. 2, the normal subjects are represented by circles, whereas the colon cancer subjects are represented by X's. The line appended to the discrimination graph in FIG. 2 illustrates how well the 2-gene model discriminates between the 2 groups. Values below and to the right of the line represent subjects predicted by the 2-gene model to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. As shown in FIG. 2, 5 normal subjects (circles) and 3 colon cancer subjects (X's) are classified in the wrong patient population.

The following equation describes the discrimination line shown in FIG. 2:

MSH6=2.861677+0.840724*PSEN2

The intercept (alpha) and slope (beta) of the discrimination line was computed as follows. A cutoff of 0.286 was used to compute alpha (equals −0.91489 in logit units).

Subjects above and to the left of this discrimination line have a predicted probability of being in the diseased group higher than the cutoff probability of 0.286.

The intercept C₀=2.81677 was computed by taking the difference between the intercepts for the 2 groups [−10.544−(10.544)=−21.088] and subtracting the log-odds of the cutoff probability (−0.91489). This quantity was then multiplied by −1/X where X is the coefficient for MSH6 (7.0494).

A ranking of the top 49 colon cancer specific genes for which gene expression profiles were obtained, from most to least significant, is shown in Table 1B. Table 1B summarizes the results of significance tests (Z-statistic and p-values) for the difference in the mean expression levels for normal subjects and subjects suffering from colon cancer. A negative Z-statistic means that the ΔC_(T) for the colon cancer subjects is less than that of the normals, i.e., genes having a negative Z-statistic are up-regulated in colon cancer subjects as compared to normal subjects. A positive Z-statistic means that the ΔC_(T) for the colon cancer subjects is higher than that of the normals, i.e., genes with a positive Z-statistic are down-regulated in colon cancer subjects as compared to normal subjects. FIG. 3 shows a graphical representation of the Z-statistic for each of the 49 genes shown in Table 1B, indicating which genes are up-regulated and down-regulated in colon cancer subjects as compared to normal subjects.

The expression values (ΔC_(T)) for the 2-gene model, MSH6 and PSEN2, for each of the 19 colon cancer samples and 48 normal subject samples used in the analysis, and their predicted probability of having colon cancer, is shown in Table 1C. As shown in Table 1C, the predicted probability of a subject having colon cancer, based on the 2-gene model, MSH6 and PSEN2, is based on a scale of 0 to 1, “0” indicating no colon cancer (i.e., normal healthy subject), “1” indicating the subject has colon cancer. A graphical representation of the predicted probabilities of a subject having colon cancer (i.e., a colon cancer index), based on this 2-gene model, is shown in FIG. 4. Such an index can be used as a tool by a practitioner (e.g., primary care physician, oncologist, etc.) for diagnosis of colon cancer and to ascertain the necessity of future screening or treatment options.

Example 4 Precision Profile™ for Inflammatory Response

Custom primers and probes were prepared for the targeted 72 genes shown in the Precision Profile™ for Inflammatory Response (shown in Table 2), selected to be informative relative to biological state of inflammation and cancer. Gene expression profiles for the 72 inflammatory response genes were analyzed using 18 of the RNA samples obtained from colon cancer subjects, and 32 of the RNA samples obtained from healthy, normal subjects, as described in Example 1.

Logistic regression models yielding the best discrimination between subjects diagnosed with colon cancer and normal subjects were generated using the enumeration and classification methodology described in Example 2. A listing of all 1 and 2-gene logistic regression models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects with at least 75% accuracy is shown in Table 2A, (read from left to right).

As shown in Table 2A, the 1 and 2-gene models are identified in the first two columns on the left side of Table 2A, ranked by their entropy R² value (shown in column 3, ranked from high to low). The number of subjects correctly classified or misclassified by each 1 or 2-gene model for each patient group (i.e., normal vs. colon cancer) is shown in columns 4-7. The percent normal subjects and percent colon cancer subjects correctly classified by the corresponding gene model is shown in columns 8 and 9. The incremental p-value for each first and second gene in the 1 or 2-gene model is shown in columns 10 and 11 (note p-values smaller than 1×10⁻¹⁷ are reported as ‘0 ’). The total number of RNA samples analyzed in each patient group (i.e., normals vs. colon cancer) after exclusion of missing values, is shown in columns 12-13. The values missing from the total sample number for normal and/or colon cancer subjects shown in columns 12-13 correspond to instances in which values were excluded from the logistic regression analysis due to reagent limitations and/or instances where replicates did not meet quality metrics.

For example, the “best” logistic regression model (defined as the model with the highest entropy R² value, as described in Example 2) based on the 72 genes included in the Precision Profile™ for Inflammatory Response is shown in the first row of Table 2A, read left to right. The first row of Table 2A lists a 2-gene model, HMOX1 and TXNRD1, capable of classifying normal subjects with 918% accuracy, and colon cancer subjects with 94.4% accuracy. All 32 normal and 18 colon cancer RNA samples were analyzed for this 2-gene model, no values were excluded. As shown in Table 2A, this 2-gene model correctly classifies 30 of the normal subjects as being in the normal patient population, and misclassifies 2 of the normal subjects as being in the colon cancer patient population. This 2-gene model correctly classifies 17 of the colon cancer subjects as being in the colon cancer patient population, and misclassifies 1 of the colon cancer subjects as being in the normal patient population. The p-value for the 1^(st) gene, HMOX1, is 2.3E-09, the incremental p-value for the second gene, TXNRD1 is 2.1E-08.

A discrimination plot of the 2-gene model, HMOX1 and TXNRD1, is shown in FIG. 5. As shown in FIG. 5, the normal subjects are represented by circles, whereas the colon cancer subjects are represented by X's. The line appended to the discrimination graph in FIG. 5 illustrates how well the 2-gene model discriminates between the 2 groups. Values to the left of the line represent subjects predicted by the 2-gene model to be in the normal population. Values to the right of the line represent subjects predicted to be in the colon cancer population. As shown in FIG. 5, 2 normal subjects (circles) and 1 colon cancer subject (X's) are classified in the wrong patient population.

The following equation describes the discrimination line shown in FIG. 5:

HMOX1=−2.9520+1.1294*TXNRD1

The intercept (alpha) and slope (beta) of the discrimination line was computed as follows. A cutoff of 0.41465 was used to compute alpha (equals −0.34478 in logit units).

Subjects to the right of this discrimination line have a predicted probability of being in the diseased group higher than the cutoff probability of 0.41465.

The intercept C₀=−2.9520 was computed by taking the difference between the intercepts for the 2 groups [−9.5916-(9.5916)=−19.1832] and subtracting the log-odds of the cutoff probability (−0.34478). This quantity was then multiplied by −1/X where X is the coefficient for HMOX1 (−6.3815).

A ranking of the top 68 inflammatory response genes for which gene expression profiles were obtained, from most to least significant, is shown in Table 2B. Table 2B summarizes the results of significance tests (p-values) for the difference in the mean expression levels for normal subjects and subjects suffering from colon cancer.

The expression values (ΔC_(T)) for the 2-gene model, HMOX1 and TXNRD1, for each of the 18 colon cancer subjects and 32 normal subject samples used in the analysis, and their predicted probability of having colon cancer is shown in Table 2C. In Table 2C, the predicted probability of a subject having colon cancer, based on the 2-gene model HMOX1 and TXNRD1, is based on a scale of 0 to 1, “0” indicating no colon cancer (i.e., normal healthy subject), “1” indicating the subject has colon cancer. This predicted probability can be used to create a colon cancer index based on the 2-gene model HMOX1 and TXNRD1, that can be used as a tool by a practitioner (e.g., primary care physician, oncologist, etc.) for diagnosis of colon cancer and to ascertain the necessity of future screening or treatment options.

Example 5 Human Cancer General Precision Profile™

Custom primers and probes were prepared for the targeted 91 genes shown in the Human Cancer Precision Profile™ (shown in Table 3), selected to be informative relative to the biological condition of human cancer, including but not limited to ovarian, breast, cervical, prostate, lung, colon, and skin cancer. Gene expression profiles for these 91 genes were analyzed using 23 of the RNA samples obtained from colon cancer subjects, and the 50 RNA samples obtained from the healthy, normal subjects, as described in Example 1.

Logistic regression models yielding the best discrimination between subjects diagnosed with colon cancer and normal subjects were generated using the enumeration and classification methodology described in Example 2. A listing of all 1 and 2-gene logistic regression models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects with at least 75% accuracy is shown in Table 3A, (read from left to right).

As shown in Table 3A, the 1 and 2-gene models are identified in the first two columns on the left side of Table 3A, ranked by their entropy R² value (shown in column 3, ranked from high to low). The number of subjects correctly classified or misclassified by each 1 or 2-gene model for each patient group (i.e., normal vs. colon cancer) is shown in columns 4-7. The percent normal subjects and percent colon cancer subjects correctly classified by the corresponding gene model is shown in columns 8 and 9. The incremental p-value for each first and second gene in the 1 or 2-gene model is shown in columns 10-11 (note p-values smaller than 1×10⁻¹⁷ are reported as ‘0 ’). The total number of RNA samples analyzed in each patient group (i.e., normals vs. colon cancer) after exclusion of missing values, is shown in columns 12 and 13. The values missing from the total sample number for normal and/or colon cancer subjects shown in columns 12-13 correspond to instances in which values were excluded from the logistic regression analysis due to reagent limitations and/or instances where replicates did not meet quality metrics.

For example, the “best” logistic regression model (defined as the model with the highest entropy R² value, as described in Example 2) based on the 91 genes included in the Human Cancer General Precision Profile™ is shown in the first row of Table 3A, read left to right. The first row of Table 3A lists a 2-gene model, ATM and CDKN2A, capable of classifying normal subjects with 88% accuracy, and colon cancer subjects with 91.3% accuracy. All 50 normal and 23 colon cancer RNA samples were analyzed for this 2-gene model, no values were excluded. As shown in Table 3A, this 2-gene model correctly classifies 44 of the normal subjects as being in the normal patient population, and misclassifies 6 of the normal subjects as being in the colon cancer patient population. This 2-gene model correctly classifies 21 of the colon cancer subjects as being in the colon cancer patient population, and misclassifies 2 of the colon cancer subjects as being in the normal patient population. The p-value for the 1^(st) gene, ATM, is 4.2E-07, the incremental p-value for the second gene, CDKN2A is 2.8E-08.

A discrimination plot of the 2-gene model, ATM and CDKN2A, is shown in FIG. 6. As shown in FIG. 6, the normal subjects are represented by circles, whereas the colon cancer subjects are represented by X's. The line appended to the discrimination graph in FIG. 6 illustrates how well the 2-gene model discriminates between the 2 groups. Values below and to the right of the line represent subjects predicted by the 2-gene model to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. As shown in FIG. 6, 6 normal subjects (circles) and 2 colon cancer subjects (X's) are classified in the wrong patient population.

The following equation describes the discrimination line shown in FIG. 6:

ATM=1.992988+0.71347*CDKN2A

The intercept (alpha) and slope (beta) of the discrimination line was computed as follows. A cutoff of 0.2123 was used to compute alpha (equals −1.31112 in logit units).

Subjects above and to the left of this discrimination line have a predicted probability of being in the diseased group higher than the cutoff probability of 0.2123.

The intercept C₀=1.992988 was computed by taking the difference between the intercepts for the 2 groups [−5.3332-(5.3332)=−10.6664] and subtracting the log-odds of the cutoff probability (−1.31112). This quantity was then multiplied by −1/X where X is the coefficient for ATM (4.6941).

A ranking of the top 79 genes for which gene expression profiles were obtained, from most to least significant is shown in Table 3B. Table 3B summarizes the results of significance tests (p-values) for the difference in the mean expression levels for normal subjects and subjects suffering from colon cancer.

The expression values (ΔC_(T)) for the 2-gene model, ATM and CDKN2A, for each of the 23 colon cancer subjects and 50 normal subject samples used in the analysis, and their predicted probability of having colon cancer is shown in Table 3C. In Table 3C, the predicted probability of a subject having colon cancer, based on the 2-gene model ATM and CDKN2A is based on a scale of 0 to 1, “0” indicating no colon cancer (i.e., normal healthy subject), “1” indicating the subject has colon cancer. This predicted probability can be used to create a colon cancer index based on the 2-gene model ATM and CDKN2A, that can be used as a tool by a practitioner (e.g., primary care physician, oncologist, etc.) for diagnosis of colon cancer and to ascertain the necessity of future screening or treatment options.

Example 6 EGR1Precision Profile™

Custom primers and probes were prepared for the targeted 39 genes shown in the Precision Profile™ for EGR1 (shown in Table 4), selected to be informative of the biological role early growth response genes play in human cancer (including but not limited to ovarian, breast, cervical, prostate, lung, colon, and skin cancer). Gene expression profiles for these 39 genes were analyzed using 22 of the RNA samples obtained from colon cancer subjects, and the 50 RNA samples obtained from normal subjects, as described in Example 1.

Logistic regression models yielding the best discrimination between subjects diagnosed with colon cancer and normal subjects were generated using the enumeration and classification methodology described in Example 2. A listing of all 2-gene logistic regression models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects with at least 75% accuracy is shown in Table 4A, (read from left to right).

As shown in Table 4A, the 2-gene models are identified in the first two columns on the left side of Table 4A, ranked by their entropy R² value (shown in column 3, ranked from high to low). The number of subjects correctly classified or misclassified by each 2-gene model for each patient group (i.e., normal vs. colon cancer) is shown in columns 4-7. The percent normal subjects and percent colon cancer subjects correctly classified by the corresponding gene model is shown in columns 8 and 9. The incremental p-value for each first and second gene in the 2-gene model is shown in columns 10-11 (note p-values smaller than 1×10⁻¹⁷ are reported as ‘0 ’). The total number of RNA samples analyzed in each patient group (i.e., normals vs. colon cancer) after exclusion of missing values, is shown in columns 12 and 13. The values missing from the total sample number for normal and/or colon cancer subjects shown in columns 12-13 correspond to instances in which values were excluded from the logistic regression analysis due to reagent limitations and/or instances where replicates did not meet quality metrics.

For example, the “best” logistic regression model (defined as the model with the highest entropy R² value, as described in Example 2) based on the 39 genes included in the Precision Profile™ for EGR1 is shown in the first row of Table 4A, read left to right. The first row of Table 4A lists a 2-gene model, NAB2 and TGFB1, capable of classifying normal subjects with 82% accuracy, and colon cancer subjects with 81.8% accuracy. All 50 normal and 22 colon cancer RNA samples were analyzed for this 2-gene model, no values were excluded. As shown in Table 4A, this 2-gene model correctly classifies 41 of the normal subjects as being in the normal patient population, and misclassifies 9 of the normal subjects as being in the colon cancer patient population. This 2-gene model correctly classifies 18 of the colon cancer subjects as being in the colon cancer patient population, and misclassifies 4 of the colon cancer subjects as being in the normal patient population. The p-value for the 1^(st) gene, NAB2, is 6.4E-09, the incremental p-value for the second gene, TGFB1 is 4.6E-07.

A ranking of the top 33 genes for which gene expression profiles were obtained, from most to least significant is shown in Table 4B. Table 4B summarizes the results of significance tests (p-values) for the difference in the mean expression levels for normal subjects and subjects suffering from colon cancer.

Example 7 Cross-Cancer Precision Profile™

Custom primers and probes were prepared for the targeted 110 genes shown in the Cross Cancer Precision Profile™ (shown in Table 5), selected to be informative relative to the biological condition of human cancer, including but not limited to ovarian, breast, cervical, prostate, lung, colon, and skin cancer. Gene expression profiles for these 110 genes were analyzed using 23 of the RNA samples obtained from colon cancer subjects, and the 50 RNA samples obtained from healthy, normal subjects, as described in Example 1.

Logistic regression models yielding the best discrimination between subjects diagnosed with colon cancer and normal subjects were generated using the enumeration and classification methodology described in Example 2. A listing of all 1 and 2-gene logistic regression models capable of distinguishing between subjects diagnosed with colon cancer and normal subjects with at least 75% accuracy is shown in Table 5A, (read from left to right).

As shown in Table 5A, the 1 and 2-gene models are identified in the first two columns on the left side of Table 5A, ranked by their entropy R² value (shown in column 3, ranked from high to low). The number of subjects correctly classified or misclassified by each 1 or 2-gene model for each patient group (i.e., normal vs. colon cancer) is shown in columns 4-7. The percent normal subjects and percent colon cancer subjects correctly classified by the corresponding gene model is shown in columns 8 and 9. The incremental p-value for each first and second gene in the 1 or 2-gene model is shown in columns 10-11 (note p-values smaller than 1×10⁻¹⁷ are reported as ‘0 ’). The total number of RNA samples analyzed in each patient group (i.e., normals vs. colon cancer) after exclusion of missing values, is shown in columns 12 and 13. The values missing from the total sample number for normal and/or colon cancer subjects shown in columns 12-13 correspond to instances in which values were excluded from the logistic regression analysis due to reagent limitations and/or instances where replicates did not meet quality metrics.

For example, the “best” logistic regression model (defined as the model with the highest entropy R² value, as described in Example 2) based on the 110 genes in the Human Cancer General Precision Profile™ is shown in the first row of Table 5A, read left to right. The first row of Table 5A lists a 2-gene model, AXIN2 and TNF, capable of classifying normal subjects with 93.9% accuracy, and colon cancer subjects with 90:5% accuracy. Forty-nine of the normal RNA samples and 21 of the colon cancer RNA samples were used to analyze this 2-gene model after exclusion of missing values. As shown in Table 5A, this 2-gene model correctly classifies 46 of the normal subjects as being in the normal patient population and misclassifies 3 of the normal subjects as being in the colon cancer population. This 2-gene model correctly classifies 19 of the colon cancer subjects as being in the colon cancer patient population, and misclassifies only 2 of the colon cancer subjects as being in the normal patient population. The p-value for the 1^(st) gene, AXIN2, is 9.0E-10, the incremental p-value for the second gene, TNF is 2.4E-05.

A discrimination plot of the 2-gene model, AXIN2 and TNF, is shown in FIG. 7. As shown in FIG. 7, the normal subjects are represented by circles, whereas the colon cancer subjects are represented by X's. The line appended to the discrimination graph in FIG. 7 illustrates how well the 2-gene model discriminates between the 2 groups. Values below and to the right of the line represent subjects predicted by the 2-gene model to be in the normal population. Values above and to the left of the line represent subjects predicted to be in the colon cancer population. As shown in FIG. 7, 3 normal subjects (circles) and only 2 colon cancer subjects (X's) are classified in the wrong patient population.

The following equation describes the discrimination line shown in FIG. 7:

AXIN2=4.9912+0.79925*TNF

The intercept (alpha) and slope (beta) of the discrimination line was computed as follows. A cutoff of 0.3966 was used to compute alpha (equals −0.41965 in logit units).

Subjects above and to the left of this discrimination line have a predicted probability of being in the diseased group higher than the cutoff probability of 0.3966.

The intercept C₀=4.9912 was computed by taking the difference between the intercepts for the 2 groups [−11.6595−(11.6595)=−23.319] and subtracting the log-odds of the cutoff probability (−0.41965). This quantity was then multiplied by −1/X where X is the coefficient for AXIN2 (4.5879).

A ranking of the top 107 genes for which gene expression profiles were obtained, from most to least significant is shown in Table 5B. Table 5B summarizes the results of significance tests (p-values) for the difference in the mean expression levels for normal subjects and subjects suffering from colon cancer.

The expression values (ΔC_(T)) for the 2-gene model, AXIN2 and TNF, for each of the 21 colon cancer subjects and 49 normal subject samples used in the analysis, and their predicted probability of having colon cancer is shown in Table 5C. In Table 5C, the predicted probability of a subject having colon cancer, based on the 2-gene model AXIN2 and TNF is based on a scale of 0 to 1, “0” indicating no colon cancer (i.e., normal healthy subject), “1” indicating the subject to has colon cancer. This predicted probability can be used to create a colon cancer index based on the 2-gene model AXIN2 and TNF, that can be used as a tool by a practitioner (e.g., primary care physician, oncologist, etc.) for diagnosis of colon cancer and to ascertain the necessity of future screening or treatment options.

These data support that Gene Expression Profiles with sufficient precision and calibration as described herein (1) can determine subsets of individuals with a known biological condition, particularly individuals with colorectal cancer or individuals with conditions related to colorectal cancer; (2) may be used to monitor the response of patients to therapy; (3) may be used to assess the efficacy and safety of therapy; and (4) may be used to guide the medical management of a patient by adjusting therapy to bring one or more relevant Gene Expression Profiles closer to a target set of values, which may be normative values or other desired or achievable values.

Gene Expression Profiles are used for characterization and monitoring of treatment efficacy of individuals with colorectal cancer, or individuals with conditions related to colorectal cancer. Use of the algorithmic and statistical approaches discussed above to achieve such identification and to discriminate in such fashion is within the scope of various embodiments herein.

The references listed below are hereby incorporated herein by reference.

REFERENCES

-   Magidson, J. GOLDMineR User's Guide (1998). Belmont, Mass.:     Statistical Innovations Inc. -   Vermunt and Magidson (2005). Latent GOLD 4.0 Technical Guide,     Belmont Mass.: Statistical Innovations. -   Vermunt and Magidson (2007). LG-Syntax™ User's Guide: Manual for     Latent GOLD® 4.5 Syntax Module, Belmont Mass.: Statistical     Innovations. -   Vermunt J. K. and J. Magidson. Latent Class Cluster Analysis     in (2002) J. A. Hagenaars and A. L. McCutcheon (eds.), Applied     Latent Class Analysis, 89-106. Cambridge: Cambridge University     Press. -   Magidson, J. “Maximum Likelihood Assessment of Clinical Trials Based     on an Ordered Categorical Response.” (1996) Drug Information     Journal, Maple Glen, Pa.: Drug Information Association, Vol. 30, No.     1, pp 143-170.

TABLE 1 Precision Profile ™ for Colorectal Cancer Gene Gene Accession Symbol Gene Name Number ACSL5 acyl-CoA synthetase long-chain family member 5 NM_016234 ACSS2 acyl-CoA synthetase short-chain family member 2 NM_018677 NM_139274 AFAP actin filament associated protein NM_021638 ALDH1A1 aldehyde dehydrogenase 1 family, member A1 NM_000689 ALX4 aristaless-like homeobox 4 NM_021926 APC adenomatosis polyposis coli NM_000038 AXIN2 axin 2 (conductin, axil) NM_004655 BAX BCL2-associated X protein NM_138761 BCL2 B-cell CLL/lymphoma 2 NM_000633 BRAF v-raf murine sarcoma viral oncogene homolog B1 NM_004333 CA2 carbonic anhydrase II NM_000067 CA4 carbonic anhydrase IV NM_000717 CA7 carbonic anhydrase VII NM_005182 CCND3 cyclin D3 NM_001760 CD44 CD44 antigen (homing function and Indian blood group system) NM_000610 CD63 CD63 antigen (melanoma 1 antigen) NM_001780 CDC2 cell division cycle 2, G1 to S and G2 to M NM_001786 CDX2 caudal type homeo box transcription factor 2 NM_001265 CFD D component of complement (adipsin) NM_001928 CFLAR CASP8 and FADD-like apoptosis regulator NM_003879 CLDN1 claudin 1 NM_021101 CXCL1 chemokine (C—X—C motif) ligand 1 (melanoma growth stimulating activity, NM_001511 alpha) DEFA6 defensin, alpha 6, Paneth cell-specific NM_001926 ERBB2 V-erb-b2 erythroblastic leukemia viral oncogene homolog 2, NM_004448 neuro/glioblastoma derived oncogene homolog (avian) ERBB3 V-erb-b2 Erythroblastic Leukemia Viral Oncogene Homolog 3 NM_001982 GADD45A growth arrest and DNA-damage-inducible, alpha NM_001924 GPX2 glutathione peroxidase 2 (gastrointestinal) NM_002083 GSK3B glycogen synthase kinase 3 beta NM_002093 GSTA2 glutathione S-transferase A2 NM_000846 GSTT2 glutathione S-transferase theta 2 NM_000854 IGF2 Putative insulin-like growth factor II associated protein. NM_000612 IGFBP4 insulin-like growth factor binding protein 4 NM_001552 IL8 interleukin 8 NM_000584 ITGA3 integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor) NM_005501 KRT19 keratin 19 NM_002276 KRT20 keratin 20 NM_019010 MGMT O-6-methylguanine-DNA methyltransferase NM_002412 MKI67 antigen identified by monoclonal antibody Ki-67 NM_002417 MLH1 mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli) NM_000249 MME membrane metallo-endopeptidase (neutral endopeptidase, enkephalinase, NM_000902 CALLA, CD10) MSH2 mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) NM_000251 MSH6 mutS homolog 6 (E. coli) NM_000179 MUTYH mutY homolog (E. coli) NM_012222 MYC v-myc myelocytomatosis viral oncogene homolog (avian) NM_002467 NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) NM_003998 NME1 non-metastatic cells 1, protein (NM23A) expressed in NM_198175 NR2E1 nuclear receptor subfamily 2, group E, member 1 NM_003269 NUAK1 NUAK family, SNF1-like kinase, 1 NM_014840 PKLR pyruvate kinase, liver and RBC NM_000298 PPARG peroxisome proliferative activated receptor, gamma NM_138712 PSEN2 presenilin 2 (Alzheimer disease 4) NM_000447 PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and NM_000963 cyclooxygenase) RGC32 response gene to complement 32 NM_014059 RPS3A ribosomal protein S3A NM_001006 S100A4 S100 calcium binding protein A4 NM_002961 S100P S100 calcium binding protein P NM_005980 SAA1 serum amyloid A1 NM_199161 SERPINB5 serpin peptidase inhibitor, clade B (ovalbumin), member 5 NM_002639 SLC25A21 solute carrier family 25 (mitochondrial oxodicarboxylate carrier), member NM_002539 21 SLURP1 secreted LY6/PLAUR domain containing 1 NM_020427 SMARCA1 SWI/SNF related, matrix associated, actin dependent regulator of NM_139035 chromatin, subfamily a, member 1 TCF4 transcription factor 4 NM_003199 TGFBR1 transforming growth factor, beta receptor I (activin A receptor type II-like NM_004612 kinase, 53 kDa) THY1 Thy-1 cell surface antigen NM_006288 TNF tumor necrosis factor (TNF superfamily, member 2) NM_000594 TP53 tumor protein p53 (Li-Fraumeni syndrome) NM_000546 VEGF vascular endothelial growth factor NM_003376 VIL1 villin 1 NM_007127 ZNF350 zinc finger protein 350 NM_021632 ZYX Zyxin NM_003461

TABLE 2 Precision Profile ™ for Inflammatory Response Gene Gene Accession Symbol Gene Name Number ADAM17 a disintegrin and metalloproteinase domain 17 (tumor necrosis factor, NM_003183 alpha, converting enzyme) ALOX5 arachidonate 5-lipoxygenase NM_000698 APAF1 apoptotic Protease Activating Factor 1 NM_013229 C1QA complement component 1, q subcomponent, alpha polypeptide NM_015991 CASP1 caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta, NM_033292 convertase) CASP3 caspase 3, apoptosis-related cysteine peptidase NM_004346 CCL3 chemokine (C-C motif) ligand 3 NM_002983 CCL5 chemokine (C-C motif) ligand 5 NM_002985 CCR3 chemokine (C-C motif) receptor 3 NM_001837 CCR5 chemokine (C-C motif) receptor 5 NM_000579 CD19 CD19 Antigen NM_001770 CD4 CD4 antigen (p55) NM_000616 CD86 CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) NM_006889 CD8A CD8 antigen, alpha polypeptide NM_001768 CSF2 colony stimulating factor 2 (granulocyte-macrophage) NM_000758 CTLA4 cytotoxic T-lymphocyte-associated protein 4 NM_005214 CXCL1 chemokine (C—X—C motif) ligand 1 (melanoma growth stimulating NM_001511 activity, alpha) CXCL10 chemokine (C—X—C moif) ligand 10 NM_001565 CXCR3 chemokine (C—X—C motif) receptor 3 NM_001504 DPP4 Dipeptidylpeptidase 4 NM_001935 EGR1 early growth response-1 NM_001964 ELA2 elastase 2, neutrophil NM_001972 GZMB granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated serine NM_004131 esterase 1) HLA-DRA major histocompatibility complex, class II, DR alpha NM_019111 HMGB1 high-mobility group box 1 NM_002128 HMOX1 heme oxygenase (decycling) 1 NM_002133 HSPA1A heat shock protein 70 NM_005345 ICAM1 Intercellular adhesion molecule 1 NM_000201 IFI16 interferon inducible protein 16, gamma NM_005531 IFNG interferon gamma NM_000619 IL10 interleukin 10 NM_000572 IL12B interleukin 12 p40 NM_002187 IL15 Interleukin 15 NM_000585 IL18 interleukin 18 NM_001562 IL18BP IL-18 Binding Protein NM_005699 IL1B interleukin 1, beta NM_000576 IL1R1 interleukin 1 receptor, type I NM_000877 IL1RN interleukin 1 receptor antagonist NM_173843 IL23A interleukin 23, alpha subunit p19 NM_016584 IL32 interleukin 32 NM_001012631 IL5 interleukin 5 (colony-stimulating factor, eosinophil) NM_000879 IL6 interleukin 6 (interferon, beta 2) NM_000600 IL8 interleukin 8 NM_000584 IRF1 interferon regulatory factor 1 NM_002198 LTA lymphotoxin alpha (TNF superfamily, member 1) NM_000595 MAPK14 mitogen-activated protein kinase 14 NM_001315 MHC2TA class II, major histocompatibility complex, transactivator NM_000246 MIF macrophage migration inhibitory factor (glycosylation-inhibiting factor) NM_002415 MMP12 matrix metallopeptidase 12 (macrophage elastase) NM_002426 MMP9 matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type NM_004994 IV collagenase) MNDA myeloid cell nuclear differentiation antigen NM_002432 MYC v-myc myelocytomatosis viral oncogene homolog (avian) NM_002467 NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NM_003998 (p105) PLA2G7 phospholipase A2, group VII (platelet-activating factor acetylhydrolase, NM_005084 plasma) PLAUR plasminogen activator, urokinase receptor NM_002659 PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and NM_000963 cyclooxygenase) PTPRC protein tyrosine phosphatase, receptor type, C NM_002838 SERPINA1 serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, NM_000295 antitrypsin), member 1 SERPINE1 serpin peptidase inhibitor, clade E (nexin, plasminogen activator NM_000602 inhibitor type 1), member 1 SSI-3 suppressor of cytokine signaling 3 NM_003955 TGFB1 transforming growth factor, beta 1 (Camurati-Engelmann disease) NM_000660 TIMP1 tissue inhibitor of metalloproteinase 1 NM_003254 TLR2 toll-like receptor 2 NM_003264 TLR4 toll-like receptor 4 NM_003266 TNF tumor necrosis factor (TNF superfamily, member 2) NM_000594 TNFRSF13B tumor necrosis factor receptor superfamily, member 13B NM_012452 TNFRSF1A tumor necrosis factor receptor superfamily, member 1A NM_001065 TNFSF5 CD40 ligand (TNF superfamily, member 5, hyper-IgM syndrome) NM_000074 TNFSF6 Fas ligand (TNF superfamily, member 6) NM_000639 TOSO Fas apoptotic inhibitory molecule 3 NM_005449 TXNRD1 thioredoxin reductase NM_003330 VEGF vascular endothelial growth factor NM_003376

TABLE 3 Human Cancer General Precision Profile ™ Gene Gene Accession Symbol Gene Name Number ABL1 v-abl Abelson murine leukemia viral oncogene homolog 1 NM_007313 ABL2 v-abl Abelson murine leukemia viral oncogene homolog 2 (arg, Abelson- NM_007314 related gene) AKT1 v-akt murine thymoma viral oncogene homolog 1 NM_005163 ANGPT1 angiopoietin 1 NM_001146 ANGPT2 angiopoietin 2 NM_001147 APAF1 Apoptotic Protease Activating Factor 1 NM_013229 ATM ataxia telangiectasia mutated (includes complementation groups A, C and NM_138293 D) BAD BCL2-antagonist of cell death NM_004322 BAX BCL2-associated X protein NM_138761 BCL2 BCL2-antagonist of cell death NM_004322 BRAF v-raf murine sarcoma viral oncogene homolog B1 NM_004333 BRCA1 breast cancer 1, early onset NM_007294 CASP8 caspase 8, apoptosis-related cysteine peptidase NM_001228 CCNE1 Cyclin E1 NM_001238 CDC25A cell division cycle 25A NM_001789 CDK2 cyclin-dependent kinase 2 NM_001798 CDK4 cyclin-dependent kinase 4 NM_000075 CDK5 Cyclin-dependent kinase 5 NM_004935 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) NM_000389 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) NM_000077 CFLAR CASP8 and FADD-like apoptosis regulator NM_003879 COL18A1 collagen, type XVIII, alpha 1 NM_030582 E2F1 E2F transcription factor 1 NM_005225 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) NM_005228 oncogene homolog, avian) EGR1 Early growth response-1 NM_001964 ERBB2 V-erb-b2 erythroblastic leukemia viral oncogene homolog 2, NM_004448 neuro/glioblastoma derived oncogene homolog (avian) FAS Fas (TNF receptor superfamily, member 6) NM_000043 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, NM_000141 keratinocyte growth factor receptor, craniofacial dysostosis 1) FOS v-fos FBJ murine osteosarcoma viral oncogene homolog NM_005252 GZMA Granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine NM_006144 esterase 3) HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog NM_005343 ICAM1 Intercellular adhesion molecule 1 NM_000201 IFI6 interferon, alpha-inducible protein 6 NM_002038 IFITM1 interferon induced transmembrane protein 1 (9-27) NM_003641 IFNG interferon gamma NM_000619 IGF1 insulin-like growth factor 1 (somatomedin C) NM_000618 IGFBP3 insulin-like growth factor binding protein 3 NM_001013398 IL18 Interleukin 18 NM_001562 IL1B Interleukin 1, beta NM_000576 IL8 interleukin 8 NM_000584 ITGA1 integrin, alpha 1 NM_181501 ITGA3 integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor) NM_005501 ITGAE integrin, alpha E (antigen CD103, human mucosal lymphocyte antigen 1; NM_002208 alpha polypeptide) ITGB1 integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 NM_002211 includes MDF2, MSK12) JUN v-jun sarcoma virus 17 oncogene homolog (avian) NM_002228 KDR kinase insert domain receptor (a type III receptor tyrosine kinase) NM_002253 MCAM melanoma cell adhesion molecule NM_006500 MMP2 matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV NM_004530 collagenase) MMP9 matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV NM_004994 collagenase) MSH2 mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) NM_000251 MYC v-myc myelocytomatosis viral oncogene homolog (avian) NM_002467 MYCL1 v-myc myelocytomatosis viral oncogene homolog 1, lung carcinoma NM_001033081 derived (avian) NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NM_003998 (p105) NME1 non-metastatic cells 1, protein (NM23A) expressed in NM_198175 NME4 non-metastatic cells 4, protein expressed in NM_005009 NOTCH2 Notch homolog 2 NM_024408 NOTCH4 Notch homolog 4 (Drosophila) NM_004557 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog NM_002524 PCNA proliferating cell nuclear antigen NM_002592 PDGFRA platelet-derived growth factor receptor, alpha polypeptide NM_006206 PLAU plasminogen activator, urokinase NM_002658 PLAUR plasminogen activator, urokinase receptor NM_002659 PTCH1 patched homolog 1 (Drosophila) NM_000264 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) NM_000314 RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 NM_002880 RB1 retinoblastoma 1 (including osteosarcoma) NM_000321 RHOA ras homolog gene family, member A NM_001664 RHOC ras homolog gene family, member C NM_175744 S100A4 S100 calcium binding protein A4 NM_002961 SEMA4D sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) NM_006378 and short cytoplasmic domain, (semaphorin) 4D SERPINB5 serpin peptidase inhibitor, clade B (ovalbumin), member 5 NM_002639 SERPINE1 serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor NM_000602 type 1), member 1 SKI v-ski sarcoma viral oncogene homolog (avian) NM_003036 SKIL SKI-like oncogene NM_005414 SMAD4 SMAD family member 4 NM_005359 SOCS1 suppressor of cytokine signaling 1 NM_003745 SRC v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) NM_198291 TERT telomerase-reverse transcriptase NM_003219 TGFB1 transforming growth factor, beta 1 (Camurati-Engelmann disease) NM_000660 THBS1 thrombospondin 1 NM_003246 TIMP1 tissue inhibitor of metalloproteinase 1 NM_003254 TIMP3 Tissue inhibitor of metalloproteinase 3 (Sorsby fundus dystrophy, NM_000362 pseudoinflammatory) TNF tumor necrosis factor (TNF superfamily, member 2) NM_000594 TNFRSF10A tumor necrosis factor receptor superfamily, member 10a NM_003844 TNFRSF10B tumor necrosis factor receptor superfamily, member 10b NM_003842 TNFRSF1A tumor necrosis factor receptor superfamily, member 1A NM_001065 TP53 tumor protein p53 (Li-Fraumeni syndrome) NM_000546 VEGF vascular endothelial growth factor NM_003376 VHL von Hippel-Lindau tumor suppressor NM_000551 WNT1 wingless-type MMTV integration site family, member 1 NM_005430 WT1 Wilms tumor 1 NM_000378

TABLE 4 Precision Profile ™ for EGR1 Gene Gene Accession Symbol Gene Name Number ALOX5 arachidonate 5-lipoxygenase NM_000698 APOA1 apolipoprotein A-I NM_000039 CCND2 cyclin D2 NM_001759 CDKN2D cyclin-dependent kinase inhibitor 2D (p19, inhibits CDK4) NM_001800 CEBPB CCAAT/enhancer binding protein (C/EBP), beta NM_005194 CREBBP CREB binding protein (Rubinstein-Taybi syndrome) NM_004380 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) NM_005228 oncogene homolog, avian) EGR1 early growth response 1 NM_001964 EGR2 early growth response 2 (Krox-20 homolog, Drosophila) NM_000399 EGR3 early growth response 3 NM_004430 EGR4 early growth response 4 NM_001965 EP300 E1A binding protein p300 NM_001429 F3 coagulation factor III (thromboplastin, tissue factor) NM_001993 FGF2 fibroblast growth factor 2 (basic) NM_002006 FN1 fibronectin 1 NM_00212482 FOS v-fos FBJ murine osteosarcoma viral oncogene homolog NM_005252 ICAM1 Intercellular adhesion molecule 1 NM_000201 JUN jun oncogene NM_002228 MAP2K1 mitogen-activated protein kinase kinase 1 NM_002755 MAPK1 mitogen-activated protein kinase 1 NM_002745 NAB1 NGFI-A binding protein 1 (EGR1 binding protein 1) NM_005966 NAB2 NGFI-A binding protein 2 (EGR1 binding protein 2) NM_005967 NFATC2 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 2 NM_173091 NFκB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NM_003998 (p105) NR4A2 nuclear receptor subfamily 4, group A, member 2 NM_006186 PDGFA platelet-derived growth factor alpha polypeptide NM_002607 PLAU plasminogen activator, urokinase NM_002658 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers NM_000314 1) RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 NM_002880 S100A6 S100 calcium binding protein A6 NM_014624 SERPINE1 serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor NM_000302 type 1), member 1 SMAD3 SMAD, mothers against DPP homolog 3 (Drosophila) NM_005902 SRC v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) NM_198291 TGFB1 transforming growth factor, beta 1 NM_000660 THBS1 thrombospondin 1 NM_003246 TOPBP1 topoisomerase (DNA) II binding protein 1 NM_007027 TNFRSF6 Fas (TNF receptor superfamily, member 6) NM_000043 TP53 tumor protein p53 (Li-Fraumeni syndrome) NM_000546 WT1 Wilms tumor 1 NM_000378

TABLE 5 Cross-Cancer Precision Profile ™ Gene Accession Gene Symbol Gene Name Number ACPP acid phosphatase, prostate NM_001099 ADAM17 a disintegrin and metalloproteinase domain 17 (tumor necrosis factor NM_003183 alpha, converting enzyme) ANLN anillin, actin binding protein (scraps homolog, Drosophila) NM_018685 APC adenomatosis polyposis coli NM_000038 AXIN2 axin 2 (conductin, axil) NM_004655 BAX BCL2-associated X protein NM_138761 BCAM basal cell adhesion molecule (Lutheran blood group) NM_005581 C1QA complement component 1, q subcomponent, alpha polypeptide NM_015991 C1QB complement component 1, q subcomponent, B chain NM_000491 CA4 carbonic anhydrase IV NM_000717 CASP3 caspase 3, apoptosis-related cysteine peptidase NM_004346 CASP9 caspase 9, apoptosis-related cysteine peptidase NM_001229 CAV1 caveolin 1, caveolae protein 22 kDa NM_001753 CCL3 chemokine (C-C motif) ligand 3 NM_002983 CCL5 chemokine (C-C motif) ligand 5 NM_002985 CCR7 chemokine (C-C motif) receptor 7 NM_001838 CD40LG CD40 ligand (TNF superfamily, member 5, hyper-IgM syndrome) NM_000074 CD59 CD59 antigen p18-20 NM_000611 CD97 CD97 molecule NM_078481 CDH1 cadherin 1, type 1, E-cadherin (epithelial) NM_004360 CEACAM1 carcinoembryonic antigen-related cell adhesion molecule 1 (biliary NM_001712 glycoprotein) CNKSR2 connector enhancer of kinase suppressor of Ras 2 NM_014927 CTNNA1 catenin (cadherin-associated protein), alpha 1, 102 kDa NM_001903 CTSD cathepsin D (lysosomal aspartyl peptidase) NM_001909 CXCL1 chemokine (C—X—C motif) ligand 1 (melanoma growth stimulating NM_001511 activity, alpha) DAD1 defender against cell death 1 NM_001344 DIABLO diablo homolog (Drosophila) NM_019887 DLC1 deleted in liver cancer 1 NM_182643 E2F1 E2F transcription factor 1 NM_005225 EGR1 early growth response-1 NM_001964 ELA2 elastase 2, neutrophil NM_001972 ESR1 estrogen receptor 1 NM_000125 ESR2 estrogen receptor 2 (ER beta) NM_001437 ETS2 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) NM_005239 FOS v-fos FBJ murine osteosarcoma viral oncogene homolog NM_005252 G6PD glucose-6-phosphate dehydrogenase NM_000402 GADD45A growth arrest and DNA-damage-inducible, alpha NM_001924 GNB1 guanine nucleotide binding protein (G protein), beta polypeptide 1 NM_002074 GSK3B glycogen synthase kinase 3 beta NM_002093 HMGA1 high mobility group AT-hook 1 NM_145899 HMOX1 heme oxygenase (decycling) 1 NM_002133 HOXA10 homeobox A10 NM_018951 HSPA1A heat shock protein 70 NM_005345 IFI16 interferon inducible protein 16, gamma NM_005531 IGF2BP2 insulin-like growth factor 2 mRNA binding protein 2 NM_006548 IGFBP3 insulin-like growth factor binding protein 3 NM_001013398 IKBKE inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase NM_014002 epsilon IL8 interleukin 8 NM_000584 ING2 inhibitor of growth family, member 2 NM_001564 IQGAP1 IQ motif containing GTPase activating protein 1 NM_003870 IRF1 interferon regulatory factor 1 NM_002198 ITGAL integrin, alpha L (antigen CD11A (p180), lymphocyte function- NM_002209 associated antigen 1; alpha polypeptide) LARGE like-glycosyltransferase NM_004737 LGALS8 lectin, galactoside-binding, soluble, 8 (galectin 8) NM_006499 LTA lymphotoxin alpha (TNF superfamily, member 1) NM_000595 MAPK14 mitogen-activated protein kinase 14 NM_001315 MCAM melanoma cell adhesion molecule NM_006500 MEIS1 Meis1, myeloid ecotropic viral integration site 1 homolog (mouse) NM_002398 MLH1 mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli) NM_000249 MME membrane metallo-endopeptidase (neutral endopeptidase, enkephalinase, NM_000902 CALLA, CD10) MMP9 matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type NM_004994 IV collagenase) MNDA myeloid cell nuclear differentiation antigen NM_002432 MSH2 mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) NM_000251 MSH6 mutS homolog 6 (E. coli) NM_000179 MTA1 metastasis associated 1 NM_004689 MTF1 metal-regulatory transcription factor 1 NM_005955 MYC v-myc myelocytomatosis viral oncogene homolog (avian) NM_002467 MYD88 myeloid differentiation primary response gene (88) NM_002468 NBEA neurobeachin NM_015678 NCOA1 nuclear receptor coactivator 1 NM_003743 NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like NM_015277 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog NM_002524 NUDT4 nudix (nucleoside diphosphate linked moiety X)-type motif 4 NM_019094 PLAU plasminogen activator, urokinase NM_002658 PLEK2 pleckstrin 2 NM_016445 PLXDC2 plexin domain containing 2 NM_032812 PPARG peroxisome proliferative activated receptor, gamma NM_138712 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers NM_000314 1) PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and NM_000963 cyclooxygenase) PTPRC protein tyrosine phosphatase, receptor type, C NM_002838 PTPRK protein tyrosine phosphatase, receptor type, K NM_002844 RBM5 RNA binding motif protein 5 NM_005778 RP5- invasion inhibitory protein 45 NM_001025374 1077B9.4 S100A11 S100 calcium binding protein A11 NM_005620 S100A4 S100 calcium binding protein A4 NM_002961 SCGB2A1 secretoglobin, family 2A, member 1 NM_002407 SERPINA1 serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, NM_000295 antitrypsin), member 1 SERPINE1 serpin peptidase inhibitor, clade E (nexin, plasminogen activator NM_000602 inhibitor type 1), member 1 SERPING1 serpin peptidase inhibitor, clade G (C1 inhibitor), member 1, NM_000062 (angioedema, hereditary) SIAH2 seven in absentia homolog 2 (Drosophila) NM_005067 SLC43A1 solute carrier family 43, member NM_003627 SP1 Sp1 transcription factor NM_138473 SPARC secreted protein, acidic, cysteine-rich (osteonectin) NM_003118 SRF serum response factor (c-fos serum response element-binding NM_003131 transcription factor) ST14 suppression of tumorigenicity 14 (colon carcinoma) NM_021978 TEGT testis enhanced gene transcript (BAX inhibitor 1) NM_003217 TGFB1 transforming growth factor, beta 1 (Camurati-Engelmann disease) NM_000660 TIMP1 tissue inhibitor of metalloproteinase 1 NM_003254 TLR2 toll-like receptor 2 NM_003264 TNF tumor necrosis factor (TNF superfamily, member 2) NM_000594 TNFRSF1A tumor necrosis factor receptor superfamily, member 1A NM_001065 TXNRD1 thioredoxin reductase NM_003330 UBE2C ubiquitin-conjugating enzyme E2C NM_007019 USP7 ubiquitin specific peptidase 7 (herpes virus-associated) NM_003470 VEGFA vascular endothelial growth factor NM_003376 VIM vimentin NM_003380 XK X-linked Kx blood group (McLeod syndrome) NM_021083 XRCC1 X-ray repair complementing defective repair in Chinese hamster cells 1 NM_006297 ZNF185 zinc finger protein 185 (LIM domain) NM_007150 ZNF350 zinc finger protein 350 NM_021632

TABLE 6 Precision Profile ™ for Immunotherapy Gene Symbol ABL1 ABL2 ADAM17 ALOX5 CD19 CD4 CD40LG CD86 CCR5 CTLA4 EGFR ERBB2 HSPA1A IFNG IL12 IL15 IL23A KIT MUC1 MYC PDGFRA PTGS2 PTPRC RAF1 TGFB1 TLR2 TNF TNFRSF10B TNFRSF13B VEGF

TABLE 1A total used Normal Colon (excludes N = 50 19 missing) 2-gene models and Entropy #normal #normal #cc #cc Correct Correct # # 1-gene models R-sq Correct FALSE Correct FALSE Classification Classification p-val 1 p-val 2 normals disease MSH6 PSEN2 0.55 42 6 16 3 87.5% 84.2% 6.6E−11 1.2E−06 48 19 CA4 MME 0.49 44 6 17 2 88.0% 89.5% 2.2E−08 1.3E−08 50 19 APC CFLAR 0.45 43 7 16 3 86.0% 84.2% 1.8E−09 2.2E−06 50 19 AXIN2 MUTYH 0.44 39 10 16 3 79.6% 84.2% 2.4E−09 0.0012 49 19 MSH6 MUTYH 0.44 43 6 16 3 87.8% 84.2% 3.0E−09 0.0001 49 19 MSH2 PSEN2 0.42 41 8 16 3 83.7% 84.2% 1.1E−08 0.0017 49 19 AXIN2 TNF 0.41 41 9 15 4 82.0% 79.0% 1.6E−06 0.0054 50 19 AXIN2 IGFBP4 0.39 42 8 16 3 84.0% 84.2% 2.2E−08 0.0095 50 19 MSH2 MUTYH 0.39 39 10 15 4 79.6% 79.0% 2.3E−08 0.0093 49 19 BAX MSH6 0.39 42 7 16 3 85.7% 84.2% 0.0011 5.6E−08 49 19 ACSL5 AXIN2 0.39 39 11 16 3 78.0% 84.2% 0.0143 2.2E−08 50 19 AXIN2 MSH2 0.38 44 6 15 4 88.0% 79.0% 0.0097 0.0149 50 19 MSH6 TNF 0.38 39 10 15 4 79.6% 79.0% 4.7E−06 0.0015 49 19 MSH2 S100P 0.38 39 11 15 4 78.0% 79.0% 4.9E−07 0.0123 50 19 MSH6 NME1 0.38 40 9 14 4 81.6% 77.8% 8.0E−08 0.0029 49 18 MSH2 NME1 0.38 39 11 15 3 78.0% 83.3% 7.9E−08 0.0178 50 18 AXIN2 PSEN2 0.37 38 11 16 3 77.6% 84.2% 8.7E−08 0.0199 49 19 ACSL5 MSH6 0.37 39 10 15 4 79.6% 79.0% 0.0021 4.5E−08 49 19 MSH6 VEGF 0.37 43 6 15 4 87.8% 79.0% 8.8E−08 0.0023 49 19 CD63 MSH6 0.37 40 9 15 4 81.6% 79.0% 0.0024 5.0E−08 49 19 APC AXIN2 0.37 40 10 15 4 80.0% 79.0% 0.0350 6.3E−05 50 19 MSH6 TP53 0.37 37 12 14 4 75.5% 77.8% 1.8E−07 0.0051 49 18 CFLAR MSH2 0.37 41 9 16 3 82.0% 84.2% 0.0229 5.0E−08 50 19 AXIN2 MSH6 0.36 43 6 15 4 87.8% 79.0% 0.0030 0.0425 49 19 MSH6 S100A4 0.36 38 10 15 4 79.2% 79.0% 2.5E−07 0.0027 48 19 AXIN2 GSK3B 0.36 43 7 15 4 86.0% 79.0% 3.4E−06 0.0415 50 19 AXIN2 MME 0.36 40 10 16 3 80.0% 84.2% 4.8E−06 0.0439 50 19 CFLAR MSH6 0.36 40 9 15 4 81.6% 79.0% 0.0035 7.0E−08 49 19 MSH2 TNF 0.36 41 9 16 3 82.0% 84.2% 1.1E−05 0.0294 50 19 MSH2 VEGF 0.36 41 9 16 3 82.0% 84.2% 1.3E−07 0.0295 50 19 MSH2 RPS3A 0.36 38 12 15 4 76.0% 79.0% 1.2E−07 0.0305 50 19 AXIN2 MYC 0.36 44 6 15 4 88.0% 79.0% 8.9E−08 0.0475 50 19 AXIN2 ZNF350 0.36 38 12 15 4 76.0% 79.0% 1.0E−05 0.0479 50 19 MSH2 S100A4 0.35 41 8 15 4 83.7% 79.0% 4.1E−07 0.0341 49 19 MSH6 S100P 0.34 37 12 15 4 75.5% 79.0% 2.6E−06 0.0098 49 19 GADD45A GSK3B 0.33 42 8 15 4 84.0% 79.0% 1.4E−05 4.9E−05 50 19 MGMT MSH6 0.33 39 9 15 4 81.3% 79.0% 0.0142 3.0E−07 48 19 IGFBP4 MSH6 0.33 42 7 15 4 85.7% 79.0% 0.0164 3.7E−07 49 19 CCND3 MSH6 0.32 38 10 15 4 79.2% 79.0% 0.0231 1.1E−06 48 19 AXIN2 0.31 40 10 15 4 80.0% 79.0% 4.9E−07 50 19 MSH6 VIL1 0.31 37 12 15 4 75.5% 79.0% 2.5E−05 0.0357 49 19 CD44 MSH6 0.31 37 12 15 4 75.5% 79.0% 0.0384 1.4E−06 49 19 MSH6 RPS3A 0.31 38 11 15 4 77.6% 79.0% 1.2E−06 0.0442 49 19 MSH2 0.30 38 12 15 4 76.0% 79.0% 7.2E−07 50 19 CA4 GSK3B 0.29 40 10 15 4 80.0% 79.0% 7.2E−05 5.7E−05 50 19 APC S100P 0.28 38 12 15 4 76.0% 79.0% 3.0E−05 0.0024 50 19 ITGA3 TNF 0.28 40 10 15 4 80.0% 79.0% 0.0004 1.9E−05 50 19 CD44 NFKB1 0.26 39 11 15 4 78.0% 79.0% 1.7E−05 9.6E−06 50 19 APC VEGF 0.26 40 10 15 4 80.0% 79.0% 9.4E−06 0.0070 50 19 APC NME1 0.26 39 11 14 4 78.0% 77.8% 1.0E−05 0.0151 50 18 MSH6 0.26 37 12 15 4 75.5% 79.0% 5.7E−06 49 19 GADD45A MME 0.24 39 11 15 4 78.0% 79.0% 0.0010 0.0027 50 19 GADD45A MLH1 0.21 42 8 15 4 84.0% 79.0% 0.0053 0.0077 50 19 ALDH1A1 TNF 0.20 40 10 15 4 80.0% 79.0% 0.0103 0.0002 50 19 CA4 NFKB1 0.19 39 11 15 4 78.0% 79.0% 0.0004 0.0043 50 19 BAX ITGA3 0.15 39 11 15 4 78.0% 79.0% 0.0040 0.0013 50 19

TABLE 1B Colon Normals Sum Group Size 27.5% 72.5% 100% N = 19 50 69  Gene Mean Mean Z-statistic p-val AXIN2 19.9 18.8 5.03 4.9E−07 MSH2 18.5 17.7 4.96 7.2E−07 MSH6 19.7 19.0 4.54 5.7E−06 APC 18.2 17.5 3.71 0.0002 GADD45A 18.8 19.5 −3.20 0.0014 TNF 18.1 18.5 −3.16 0.0016 ZNF350 19.6 19.1 3.13 0.0018 MLH1 18.0 17.5 3.10 0.0019 MME 15.4 14.8 2.91 0.0036 GSK3B 16.2 15.8 2.81 0.0050 CA4 18.1 18.8 −2.72 0.0065 VIL1 19.9 20.6 −2.69 0.0072 TGFBR1 18.6 18.3 2.57 0.0103 CA2 16.3 16.7 −2.39 0.0167 S100P 16.3 17.2 −2.35 0.0189 BCL2 16.4 16.1 2.06 0.0397 ITGA3 22.2 21.9 2.03 0.0427 NFKB1 16.9 16.7 1.72 0.0859 ALDH1A1 18.6 18.3 1.69 0.0914 S100A4 12.9 13.1 −1.68 0.0932 IL8 22.2 21.7 1.64 0.1010 BAX 15.3 15.5 −1.43 0.1529 CCND3 14.1 14.3 −1.38 0.1673 CD44 13.7 13.9 −1.34 0.1791 ACSS2 19.3 19.1 1.29 0.1959 AFAP 18.3 18.1 1.25 0.2118 PSEN2 19.4 19.6 −1.22 0.2230 VEGF 23.0 23.3 −1.18 0.2395 CFD 13.8 14.1 −1.11 0.2684 RPS3A 15.9 16.1 −1.10 0.2697 TP53 16.0 15.9 1.03 0.3039 ERBB2 22.4 22.2 1.02 0.3078 ZYX 12.1 12.3 −1.00 0.3173 NME1 19.2 19.3 −0.93 0.3537 IGFBP4 21.3 21.4 −0.83 0.4078 CXCL1 19.1 19.3 −0.81 0.4202 BRAF 17.2 17.1 0.80 0.4233 MYC 18.2 18.1 0.80 0.4247 TCF4 19.6 19.5 0.78 0.4363 RGC32 18.0 17.9 0.57 0.5685 CD63 15.0 15.0 −0.42 0.6754 NUAK1 23.4 23.5 −0.42 0.6774 PTGS2 17.1 17.1 −0.42 0.6780 MUTYH 19.4 19.4 −0.39 0.6961 MGMT 19.4 19.5 −0.36 0.7156 IGF2 21.4 21.5 −0.33 0.7407 MKI67 22.2 22.2 −0.15 0.8792 ACSL5 17.8 17.8 0.15 0.8832 CFLAR 14.8 14.8 0.13 0.9000

TABLE 1C Predicted probability of colon Patient ID Group MSH6 PSEN2 logit odds cancer CC-017 Colon 21.71 19.51 16.26 1.2E+07 1.0000 CC-019 Colon 19.86 18.65 8.35 4.2E+03 0.9998 CC-020 Colon 20.14 19.14 7.47 1.8E+03 0.9994 CC-007 Colon 20.91 20.20 6.60 7.3E+02 0.9986 CC-003 Colon 19.35 18.41 6.25 5.2E+02 0.9981 CC-011 Colon 19.52 19.19 2.75 1.6E+01 0.9400 CC-005 Colon 20.21 20.04 2.61 1.4E+01 0.9314 CC-014 Colon 19.83 19.65 2.22 9.2E+00 0.9020 CC-012 Colon 19.70 19.58 1.74 5.7E+00 0.8506 CC-013 Colon 19.76 19.72 1.33 3.8E+00 0.7916 CC-002 Colon 19.05 18.89 1.30 3.7E+00 0.7851 CC-006 Colon 19.65 19.62 1.12 3.1E+00 0.7542 CC-009 Colon 19.07 18.98 0.85 2.3E+00 0.6998 CC-010 Colon 20.30 20.47 0.65 1.9E+00 0.6569 HN-036-CC Normal 18.90 18.83 0.60 1.8E+00 0.6465 HN-014-CC Normal 19.26 19.30 0.29 1.3E+00 0.5710 HN-049-CC Normal 19.58 19.70 0.16 1.2E+00 0.5404 CC-008 Colon 19.82 19.99 0.13 1.1E+00 0.5335 HN-046-CC Normal 18.86 18.88 −0.05 9.5E−01 0.4877 HN-030-CC Normal 19.82 20.05 −0.23 7.9E−01 0.4417 HN-004-CC Normal 18.76 18.90 −0.86 4.2E−01 0.2964 CC-018 Colon 18.85 19.01 −0.90 4.1E−01 0.2895 HN-001-CC Normal 19.88 20.24 −0.93 3.9E−01 0.2829 HN-029-CC Normal 19.81 20.17 −0.96 3.8E−01 0.2760 HN-008-CC Normal 18.62 18.81 −1.31 2.7E−01 0.2127 HN-035-CC Normal 19.00 19.27 −1.35 2.6E−01 0.2056 HN-047-CC Normal 18.89 19.14 −1.36 2.6E−01 0.2041 HN-009-CC Normal 18.87 19.16 −1.60 2.0E−01 0.1679 HN-033-CC Normal 20.00 20.53 −1.80 1.7E−01 0.1416 HN-026-CC Normal 19.27 19.67 −1.84 1.6E−01 0.1369 CC-015 Colon 19.22 19.61 −1.86 1.6E−01 0.1344 HN-034-CC Normal 19.37 19.81 −1.96 1.4E−01 0.1236 HN-013-CC Normal 18.97 19.35 −2.00 1.4E−01 0.1191 CC-004 Colon 19.24 19.67 −2.03 1.3E−01 0.1162 HN-044-CC Normal 18.53 18.86 −2.27 1.0E−01 0.0935 HN-041-CC Normal 19.00 19.47 −2.54 7.9E−02 0.0728 HN-024-CC Normal 19.48 20.05 −2.56 7.7E−02 0.0716 HN-010-CC Normal 19.00 19.48 −2.63 7.2E−02 0.0671 HN-040-CC Normal 19.40 19.97 −2.67 6.9E−02 0.0647 HN-048-CC Normal 18.68 19.14 −2.83 5.9E−02 0.0555 CC-001 Colon 18.37 18.78 −2.93 5.4E−02 0.0508 HN-032-CC Normal 19.20 19.79 −2.98 5.1E−02 0.0485 HN-025-CC Normal 18.95 19.53 −3.24 3.9E−02 0.0376 HN-050-CC Normal 19.05 19.65 −3.31 3.7E−02 0.0353 HN-015-CC Normal 18.93 19.54 −3.49 3.1E−02 0.0296 HN-011-CC Normal 19.04 19.75 −3.88 2.1E−02 0.0201 HN-016-CC Normal 19.37 20.17 −4.10 1.7E−02 0.0162 HN-039-CC Normal 18.42 19.06 −4.18 1.5E−02 0.0151 HN-038-CC Normal 18.61 19.31 −4.34 1.3E−02 0.0129 HN-031-CC Normal 18.84 19.63 −4.64 9.6E−03 0.0095 HN-022-CC Normal 19.98 21.01 −4.72 8.9E−03 0.0088 HN-003-CC Normal 18.85 19.70 −4.93 7.2E−03 0.0072 HN-019-CC Normal 18.77 19.62 −5.05 6.4E−03 0.0064 HN-023-CC Normal 18.52 19.33 −5.08 6.2E−03 0.0062 HN-043-CC Normal 18.59 19.42 −5.12 6.0E−03 0.0060 HN-045-CC Normal 18.77 19.64 −5.16 5.7E−03 0.0057 HN-027-CC Normal 18.73 19.62 −5.33 4.9E−03 0.0048 HN-021-CC Normal 18.49 19.34 −5.38 4.6E−03 0.0046 HN-018-CC Normal 18.46 19.34 −5.57 3.8E−03 0.0038 HN-028-CC Normal 19.05 20.05 −5.65 3.5E−03 0.0035 HN-012-CC Normal 18.64 19.57 −5.66 3.5E−03 0.0035 HN-006-CC Normal 18.52 19.45 −5.86 2.9E−03 0.0029 HN-042-CC Normal 18.35 19.26 −5.88 2.8E−03 0.0028 HN-005-CC Normal 18.36 19.38 −6.52 1.5E−03 0.0015 HN-020-CC Normal 18.26 19.50 −7.96 3.5E−04 0.0003 HN-007-CC Normal 18.08 19.38 −8.44 2.2E−04 0.0002 HN-017-CC Normal 18.93 20.51 −9.22 9.9E−05 0.0001

TABLE 2a total used Normal Colon (excludes En- N = 32 18 missing) 2-gene models and tropy #normal #normal #ci #ci Correct Correct # # 1-gene models R-sq Correct FALSE Correct FALSE Classification Classification p-val 1 p-val 2 normals disease HMOX1 TXNRD1 0.67 30 2 17 1 93.8% 94.4% 2.3E−09 2.1E−08 32 18 C1QA LTA 0.61 28 4 16 2 87.5% 88.9% 8.3E−08 0.0017 32 18 DPP4 IL32 0.60 29 3 16 2 90.6% 88.9% 5.7E−09 6.3E−08 32 18 C1QA TXNRD1 0.59 29 3 16 2 90.6% 88.9% 3.9E−08 0.0030 32 18 CCR5 DPP4 0.58 28 4 16 2 87.5% 88.9% 1.4E−07 6.1E−08 32 18 C1QA PTGS2 0.57 29 3 16 2 90.6% 88.9% 2.5E−09 0.0060 32 18 APAF1 C1QA 0.57 29 3 16 2 90.6% 88.9% 0.0069 5.5E−08 32 18 CCR5 LTA 0.56 30 2 16 2 93.8% 88.9% 3.8E−07 1.0E−07 32 18 C1QA PTPRC 0.55 27 5 16 2 84.4% 88.9% 6.1E−09 0.0118 32 18 C1QA TNFRSF13 0.55 27 5 15 3 84.4% 83.3% 9.6E−09 0.0118 32 18 C1QA IL8 0.55 29 3 16 2 90.6% 88.9% 7.3E−07 0.0122 32 18 C1QA TLR4 0.55 26 6 16 2 81.3% 88.9% 7.8E−09 0.0126 32 18 C1QA CASP3 0.54 30 2 16 2 93.8% 88.9% 2.7E−07 0.0160 32 18 C1QA HSPA1A 0.54 30 2 16 2 93.8% 88.9% 2.8E−09 0.0173 32 18 TGFB1 TXNRD1 0.54 29 3 15 3 90.6% 83.3% 2.4E−07 1.6E−06 32 18 APAF1 PLAUR 0.54 28 4 16 2 87.5% 88.9% 2.7E−07 1.6E−07 32 18 C1QA MMP12 0.53 29 3 16 2 90.6% 88.9% 3.6E−09 0.0234 32 18 C1QA IL5 0.53 26 6 15 3 81.3% 83.3% 5.9E−09 0.0250 32 18 C1QA IL15 0.52 27 5 15 3 84.4% 83.3% 2.0E−08 0.0406 32 18 CCL5 LTA 0.51 28 4 16 2 87.5% 88.9% 1.8E−06 8.2E−06 32 18 CCR5 TNFSF5 0.51 27 5 15 3 84.4% 83.3% 9.0E−07 5.0E−07 32 18 TNF TNFSF5 0.51 28 4 16 2 87.5% 88.9% 9.1E−07 6.4E−06 32 18 CD4 TGFB1 0.51 29 3 15 3 90.6% 83.3% 4.1E−06 1.2E−08 32 18 LTA TNF 0.50 28 4 16 2 87.5% 88.9% 9.6E−06 2.8E−06 32 18 NFKB1 TGFB1 0.50 31 1 15 3 96.9% 83.3% 6.2E−06 2.2E−08 32 18 HMOX1 PTPRC 0.49 29 3 16 2 90.6% 88.9% 4.6E−08 1.0E−05 32 18 LTA TGFB1 0.49 27 5 15 3 84.4% 83.3% 7.6E−06 4.2E−06 32 18 APAF1 TLR2 0.48 28 4 16 2 87.5% 88.9% 1.5E−07 1.2E−06 32 18 MAPK14 TXNRD1 0.47 25 7 15 3 78.1% 83.3% 2.3E−06 3.7E−08 32 18 PLAUR TXNRD1 0.47 28 4 15 3 87.5% 83.3% 2.5E−06 2.8E−06 32 18 TIMP1 TXNRD1 0.47 26 6 15 3 81.3% 83.3% 2.6E−06 1.8E−07 32 18 APAF1 TGFB1 0.46 28 4 15 3 87.5% 83.3% 2.2E−05 2.1E−06 32 18 HMOX1 NFKB1 0.46 26 6 15 3 81.3% 83.3% 8.1E−08 3.4E−05 32 18 C1QA 0.46 28 4 15 3 87.5% 83.3% 4.9E−08 32 18 HMOX1 LTA 0.45 26 6 15 3 81.3% 83.3% 1.5E−05 3.7E−05 32 18 IL32 TNFSF5 0.45 26 6 15 3 81.3% 83.3% 6.9E−06 7.9E−07 32 18 IL32 TOSO 0.45 27 4 16 2 87.1% 88.9% 9.2E−07 1.2E−06 31 18 ICAM1 TXNRD1 0.45 27 5 15 3 84.4% 83.3% 4.3E−06 1.1E−06 32 18 APAF1 HMOX1 0.45 27 5 16 2 84.4% 88.9% 4.5E−05 3.0E−06 32 18 APAF1 CASP1 0.44 26 6 15 3 81.3% 83.3% 5.7E−07 3.5E−06 32 18 CCL5 TNFSF5 0.44 27 5 15 3 84.4% 83.3% 9.8E−06 9.4E−05 32 18 DPP4 TNF 0.44 27 5 15 3 84.4% 83.3% 7.1E−05 1.3E−05 32 18 IL18BP TOSO 0.44 25 5 15 3 83.3% 83.3% 2.6E−06 6.2E−07 30 18 CCL5 TOSO 0.43 25 6 15 3 80.7% 83.3% 1.7E−06 0.0002 31 18 CCR5 TOSO 0.43 24 7 14 4 77.4% 77.8% 2.1E−06 2.2E−05 31 18 CCL5 DPP4 0.42 27 5 15 3 84.4% 83.3% 2.7E−05 0.0002 32 18 TGFB1 TNFSF5 0.42 26 6 14 4 81.3% 77.8% 2.5E−05 9.8E−05 32 18 IL32 LTA 0.42 27 5 15 3 84.4% 83.3% 5.5E−05 2.9E−06 32 18 ADAM17 HMOX1 0.41 26 6 15 3 81.3% 83.3% 0.0002 2.1E−06 32 18 CCR5 TXNRD1 0.41 26 6 15 3 81.3% 83.3% 2.0E−05 2.0E−05 32 18 DPP4 TGFB1 0.41 27 5 14 4 84.4% 77.8% 0.0001 4.8E−05 32 18 PLAUR PTGS2 0.40 26 6 15 3 81.3% 83.3% 7.1E−07 2.4E−05 32 18 ADAM17 CASP1 0.40 26 6 15 3 81.3% 83.3% 2.4E−06 2.7E−06 32 18 CD4 HMOX1 0.40 26 6 15 3 81.3% 83.3% 0.0002 4.4E−07 32 18 CASP1 TXNRD1 0.40 26 6 15 3 81.3% 83.3% 2.4E−05 2.6E−06 32 18 ALOX5 TXNRD1 0.40 25 7 14 4 78.1% 77.8% 2.6E−05 5.3E−07 32 18 MHC2TA TNFSF5 0.40 29 3 15 3 90.6% 83.3% 5.1E−05 1.2E−05 32 18 IL18BP LTA 0.39 27 4 15 3 87.1% 83.3% 0.0001 1.8E−06 31 18 TNF TXNRD1 0.39 27 5 15 3 84.4% 83.3% 3.1E−05 0.0004 32 18 MYC TNF 0.39 27 5 15 3 84.4% 83.3% 0.0004 1.1E−06 32 18 CCL5 MYC 0.39 25 7 15 3 78.1% 83.3% 1.1E−06 0.0006 32 18 SERPINA1 TXNRD1 0.39 26 6 15 3 81.3% 83.3% 3.7E−05 7.7E−07 32 18 MHC2TA PLA2G7 0.39 28 4 15 3 87.5% 83.3% 2.5E−05 1.7E−05 32 18 HMOX1 TNFSF5 0.38 29 3 15 3 90.6% 83.3% 8.5E−05 0.0005 32 18 DPP4 HMOX1 0.38 28 4 16 2 87.5% 88.9% 0.0005 0.0001 32 18 APAF1 MNDA 0.38 25 7 15 3 78.1% 83.3% 2.6E−06 3.2E−05 32 18 NFKB1 PLAUR 0.38 27 5 15 3 84.4% 83.3% 5.5E−05 1.1E−06 32 18 EGR1 IL8 0.38 28 4 14 4 87.5% 77.8% 0.0003 0.0051 32 18 DPP4 IL18BP 0.38 25 6 15 3 80.7% 83.3% 3.1E−06 0.0001 31 18 HMOX1 PLA2G7 0.37 26 6 15 3 81.3% 83.3% 4.0E−05 0.0006 32 18 DPP4 MHC2TA 0.37 25 7 15 3 78.1% 83.3% 2.8E−05 0.0002 32 18 EGR1 LTA 0.37 24 8 14 4 75.0% 77.8% 0.0003 0.0070 32 18 MNDA TXNRD1 0.37 26 6 15 3 81.3% 83.3% 7.0E−05 3.7E−06 32 18 EGR1 MHC2TA 0.37 26 6 15 3 81.3% 83.3% 3.1E−05 0.0073 32 18 PTPRC TNF 0.37 27 5 15 3 84.4% 83.3% 0.0010 3.1E−06 32 18 LTA PLAUR 0.37 26 6 15 3 81.3% 83.3% 8.0E−05 0.0003 32 18 EGR1 PLAUR 0.37 25 7 14 4 78.1% 77.8% 8.9E−05 0.0084 32 18 TNF TOSO 0.36 25 6 14 4 80.7% 77.8% 1.9E−05 0.0012 31 18 EGR1 HMOX1 0.36 28 4 14 4 87.5% 77.8% 0.0010 0.0099 32 18 HMOX1 HSPA1A 0.36 26 6 15 3 81.3% 83.3% 1.4E−06 0.0010 32 18 IL1RN TXNRD1 0.36 28 4 16 2 87.5% 88.9% 0.0001 3.5E−06 32 18 CCR5 CTLA4 0.36 26 6 14 4 81.3% 77.8% 5.4E−06 0.0001 32 18 CCL5 TXNRD1 0.35 25 7 15 3 78.1% 83.3% 0.0001 0.0022 32 18 TLR2 TXNRD1 0.35 25 7 15 3 78.1% 83.3% 0.0001 8.9E−06 32 18 PLAUR TLR4 0.35 25 7 14 4 78.1% 77.8% 6.2E−06 0.0001 32 18 IRF1 LTA 0.35 24 8 14 4 75.0% 77.8% 0.0005 2.7E−05 32 18 EGR1 TLR2 0.35 27 5 14 4 84.4% 77.8% 1.0E−05 0.0144 32 18 EGR1 TXNRD1 0.35 26 6 14 4 81.3% 77.8% 0.0001 0.0148 32 18 CASP3 PLAUR 0.35 24 8 14 4 75.0% 77.8% 0.0002 0.0002 32 18 PTPRC TGFB1 0.35 26 6 14 4 81.3% 77.8% 0.0010 6.0E−06 32 18 TGFB1 TOSO 0.35 24 7 15 3 77.4% 83.3% 3.2E−05 0.0023 31 18 SSI3 TXNRD1 0.35 25 7 15 3 78.1% 83.3% 0.0002 2.0E−05 32 18 CASP3 HMOX1 0.35 29 3 15 3 90.6% 83.3% 0.0017 0.0002 32 18 TNFRSF1A TXNRD1 0.34 27 5 15 3 84.4% 83.3% 0.0002 2.8E−06 32 18 CASP1 CASP3 0.34 25 7 14 4 78.1% 77.8% 0.0003 1.9E−05 32 18 MMP9 TXNRD1 0.34 26 6 15 3 81.3% 83.3% 0.0002 4.5E−06 32 18 IFI16 IL8 0.34 27 5 15 3 84.4% 83.3% 0.0012 0.0002 32 18 EGR1 TNFSF5 0.33 24 8 14 4 75.0% 77.8% 0.0004 0.0274 32 18 ADAM17 PLAUR 0.33 26 6 14 4 81.3% 77.8% 0.0003 2.8E−05 32 18 CXCR3 TNFSF5 0.33 25 7 14 4 78.1% 77.8% 0.0005 5.0E−06 32 18 CXCR3 DPP4 0.33 26 6 14 4 81.3% 77.8% 0.0006 5.1E−06 32 18 TGFB1 TNFRSF13 0.33 24 8 14 4 75.0% 77.8% 1.7E−05 0.0019 32 18 EGR1 IL10 0.33 26 6 15 3 81.3% 83.3% 0.0001 0.0312 32 18 ICAM1 LTA 0.33 26 6 15 3 81.3% 83.3% 0.0011 7.0E−05 32 18 IFI16 LTA 0.33 25 7 14 4 78.1% 77.8% 0.0012 0.0002 32 18 IL1R1 PLAUR 0.32 25 7 14 4 78.1% 77.8% 0.0004 1.8E−05 32 18 IL8 TGFB1 0.32 27 5 15 3 84.4% 83.3% 0.0024 0.0018 32 18 CCR5 EGR1 0.32 25 7 14 4 78.1% 77.8% 0.0394 0.0003 32 18 EGR1 PTPRC 0.32 24 8 14 4 75.0% 77.8% 1.4E−05 0.0409 32 18 LTA MHC2TA 0.32 26 6 15 3 81.3% 83.3% 0.0002 0.0014 32 18 HMOX1 MYC 0.32 26 6 15 3 81.3% 83.3% 1.1E−05 0.0038 32 18 EGR1 TNF 0.32 25 7 14 4 78.1% 77.8% 0.0052 0.0431 32 18 CCR5 MIF 0.32 25 7 14 4 78.1% 77.8% 1.4E−05 0.0004 32 18 CASP3 TGFB1 0.32 25 7 15 3 78.1% 83.3% 0.0028 0.0006 32 18 CASP1 EGR1 0.32 29 3 14 4 90.6% 77.8% 0.0455 3.9E−05 32 18 CTLA4 TGFB1 0.32 26 6 14 4 81.3% 77.8% 0.0029 1.9E−05 32 18 ADAM17 TGFB1 0.32 26 6 15 3 81.3% 83.3% 0.0030 4.6E−05 32 18 IRF1 TXNRD1 0.32 27 5 14 4 84.4% 77.8% 0.0004 7.9E−05 32 18 HMOX1 TNFRSF13 0.32 25 7 14 4 78.1% 77.8% 2.7E−05 0.0043 32 18 PLA2G7 PLAUR 0.32 24 8 14 4 75.0% 77.8% 0.0005 0.0003 32 18 TGFB1 TLR4 0.32 26 6 14 4 81.3% 77.8% 2.2E−05 0.0033 32 18 HMOX1 IL1R1 0.32 28 4 15 3 87.5% 83.3% 2.5E−05 0.0049 32 18 CASP3 CCR5 0.32 26 6 14 4 81.3% 77.8% 0.0005 0.0007 32 18 HMOX1 IL18 0.32 27 5 14 4 84.4% 77.8% 4.7E−05 0.0049 32 18 CASP3 TLR2 0.31 25 7 14 4 78.1% 77.8% 3.4E−05 0.0007 32 18 CCL5 PTPRC 0.31 27 5 15 3 84.4% 83.3% 1.9E−05 0.0092 32 18 TNF TNFRSF13 0.31 25 7 14 4 78.1% 77.8% 3.1E−05 0.0069 32 18 APAF1 TNF 0.31 24 8 14 4 75.0% 77.8% 0.0072 0.0003 32 18 HMOX1 TLR4 0.31 26 6 15 3 81.3% 83.3% 2.5E−05 0.0054 32 18 HMOX1 TOSO 0.31 27 4 16 2 87.1% 88.9% 0.0001 0.0056 31 18 DPP4 IFI16 0.31 25 7 15 3 78.1% 83.3% 0.0004 0.0013 32 18 CXCR3 TOSO 0.31 25 6 15 3 80.7% 83.3% 0.0001 1.4E−05 31 18 APAF1 ICAM1 0.31 25 7 14 4 78.1% 77.8% 0.0001 0.0004 32 18 APAF1 CCR5 0.31 26 6 14 4 81.3% 77.8% 0.0006 0.0004 32 18 NFKB1 TNF 0.31 24 8 14 4 75.0% 77.8% 0.0093 1.4E−05 32 18 IL8 LTA 0.31 26 6 14 4 81.3% 77.8% 0.0026 0.0036 32 18 CASP3 IL10 0.30 26 6 14 4 81.3% 77.8% 0.0003 0.0010 32 18 PLA2G7 TNF 0.30 25 7 14 4 78.1% 77.8% 0.0104 0.0004 32 18 IL1B TXNRD1 0.30 28 4 16 2 87.5% 88.9% 0.0008 2.1E−05 32 18 HMOX1 IL8 0.30 25 7 15 3 78.1% 83.3% 0.0043 0.0083 32 18 CCR5 PLA2G7 0.30 24 8 14 4 75.0% 77.8% 0.0005 0.0008 32 18 CD8A LTA 0.30 24 8 14 4 75.0% 77.8% 0.0032 2.0E−05 32 18 CCL5 CD4 0.30 27 5 14 4 84.4% 77.8% 1.4E−05 0.0165 32 18 MYC TGFB1 0.30 25 7 14 4 78.1% 77.8% 0.0063 2.6E−05 32 18 CASP3 TNF 0.30 24 8 14 4 75.0% 77.8% 0.0127 0.0013 32 18 CTLA4 IL32 0.30 26 6 14 4 81.3% 77.8% 0.0002 4.3E−05 32 18 IFI16 TXNRD1 0.30 24 8 14 4 75.0% 77.8% 0.0009 0.0007 32 18 CD8A DPP4 0.30 24 8 14 4 75.0% 77.8% 0.0022 2.2E−05 32 18 IL10 TXNRD1 0.29 26 6 14 4 81.3% 77.8% 0.0010 0.0005 32 18 HMOX1 MIF 0.29 26 6 15 3 81.3% 83.3% 3.6E−05 0.0108 32 18 IL8 PLAUR 0.29 28 4 16 2 87.5% 88.9% 0.0011 0.0058 32 18 PLAUR TNFSF5 0.29 26 6 14 4 81.3% 77.8% 0.0019 0.0011 32 18 CASP1 TLR4 0.29 25 7 14 4 78.1% 77.8% 5.5E−05 0.0001 32 18 PLA2G7 TGFB1 0.29 24 8 14 4 75.0% 77.8% 0.0085 0.0007 32 18 IFI16 TNFSF5 0.29 27 5 14 4 84.4% 77.8% 0.0021 0.0009 32 18 CCL5 PLAUR 0.29 25 7 14 4 78.1% 77.8% 0.0014 0.0250 32 18 CCR5 PTPRC 0.29 28 4 14 4 87.5% 77.8% 5.3E−05 0.0013 32 18 CASP3 LTA 0.28 25 7 14 4 78.1% 77.8% 0.0056 0.0021 32 18 CASP3 CCL5 0.28 26 6 14 4 81.3% 77.8% 0.0285 0.0022 32 18 HMOX1 PTGS2 0.28 26 6 15 3 81.3% 83.3% 4.4E−05 0.0162 32 18 DPP4 IRF1 0.28 25 7 14 4 78.1% 77.8% 0.0003 0.0037 32 18 CCL5 TNFRSF13 0.28 25 7 14 4 78.1% 77.8% 9.5E−05 0.0307 32 18 DPP4 PLAUR 0.28 25 7 14 4 78.1% 77.8% 0.0017 0.0039 32 18 CD19 MHC2TA 0.28 26 6 15 3 81.3% 83.3% 0.0007 5.9E−05 32 18 IL8 IRF1 0.28 26 6 14 4 81.3% 77.8% 0.0003 0.0091 32 18 CCL5 MIF 0.28 25 7 14 4 78.1% 77.8% 6.2E−05 0.0363 32 18 CASP1 IL15 0.28 25 7 15 3 78.1% 83.3% 7.9E−05 0.0002 32 18 CASP1 IL18 0.28 24 8 14 4 75.0% 77.8% 0.0002 0.0002 32 18 CCL5 IL8 0.27 26 6 14 4 81.3% 77.8% 0.0110 0.0401 32 18 CCL5 NFKB1 0.27 24 8 14 4 75.0% 77.8% 4.1E−05 0.0411 32 18 CCR5 TNFRSF13 0.27 25 7 14 4 78.1% 77.8% 0.0001 0.0020 32 18 CCL5 HMOX1 0.27 25 7 14 4 78.1% 77.8% 0.0230 0.0428 32 18 APAF1 IL10 0.27 26 6 15 3 81.3% 83.3% 0.0010 0.0013 32 18 HSPA1A PLAUR 0.27 27 5 15 3 84.4% 83.3% 0.0023 2.6E−05 32 18 IL8 TNF 0.27 24 8 14 4 75.0% 77.8% 0.0317 0.0118 32 18 CCL5 SSI3 0.27 26 6 15 3 81.3% 83.3% 0.0003 0.0437 32 18 IL8 MHC2TA 0.27 26 6 15 3 81.3% 83.3% 0.0009 0.0120 32 18 APAF1 TIMP1 0.27 24 8 14 4 75.0% 77.8% 0.0001 0.0014 32 18 IL1R1 TGFB1 0.27 24 8 14 4 75.0% 77.8% 0.0167 0.0001 32 18 HMOX1 IL15 0.27 28 4 15 3 87.5% 83.3% 9.6E−05 0.0251 32 18 IL10 PLA2G7 0.27 27 5 15 3 84.4% 83.3% 0.0014 0.0011 32 18 CCL5 IFI16 0.27 27 5 15 3 84.4% 83.3% 0.0018 0.0495 32 18 CCL5 CTLA4 0.27 27 5 15 3 84.4% 83.3% 0.0001 0.0498 32 18 CCR5 CD19 0.27 25 7 14 4 78.1% 77.8% 8.7E−05 0.0024 32 18 IL8 PLA2G7 0.27 24 8 14 4 75.0% 77.8% 0.0015 0.0137 32 18 IL23A TGFB1 0.27 24 8 14 4 75.0% 77.8% 0.0186 7.2E−05 32 18 PTPRC TIMP1 0.27 24 8 14 4 75.0% 77.8% 0.0002 9.7E−05 32 18 ADAM17 TLR2 0.27 25 7 14 4 78.1% 77.8% 0.0002 0.0003 32 18 ICAM1 NFKB1 0.27 26 6 15 3 81.3% 83.3% 5.5E−05 0.0006 32 18 CXCL1 HMOX1 0.26 24 8 14 4 75.0% 77.8% 0.0318 4.5E−05 32 18 GZMB LTA 0.26 26 6 14 4 81.3% 77.8% 0.0114 9.3E−05 32 18 CCR5 IL8 0.26 27 5 14 4 84.4% 77.8% 0.0169 0.0029 32 18 TLR2 TLR4 0.26 25 7 14 4 78.1% 77.8% 0.0001 0.0002 32 18 DPP4 ICAM1 0.26 25 7 14 4 78.1% 77.8% 0.0008 0.0082 32 18 HMOX1 SERPINA1 0.26 25 7 14 4 78.1% 77.8% 6.5E−05 0.0411 32 18 CASP1 IL8 0.26 25 7 14 4 78.1% 77.8% 0.0214 0.0004 32 18 MHC2TA TXNRD1 0.26 27 5 15 3 84.4% 83.3% 0.0037 0.0015 32 18 HLADRA LTA 0.26 26 6 15 3 81.3% 83.3% 0.0154 0.0003 32 18 IL32 MIF 0.26 27 5 14 4 84.4% 77.8% 0.0001 0.0007 32 18 HMOX1 TNFRSF1A 0.25 26 6 15 3 81.3% 83.3% 5.9E−05 0.0485 32 18 HLADRA IL8 0.25 24 8 14 4 75.0% 77.8% 0.0244 0.0003 32 18 IL8 TXNRD1 0.25 25 7 14 4 78.1% 77.8% 0.0042 0.0251 32 18 IL32 TXNRD1 0.25 25 7 15 3 78.1% 83.3% 0.0043 0.0008 32 18 CD4 PLAUR 0.25 24 8 14 4 75.0% 77.8% 0.0050 7.4E−05 32 18 CD19 TGFB1 0.25 24 8 14 4 75.0% 77.8% 0.0374 0.0002 32 18 DPP4 IL10 0.25 26 6 14 4 81.3% 77.8% 0.0025 0.0134 32 18 ADAM17 IRF1 0.25 24 8 15 3 75.0% 83.3% 0.0010 0.0006 32 18 IL8 MNDA 0.25 26 6 15 3 81.3% 83.3% 0.0003 0.0332 32 18 ADAM17 IL10 0.24 24 8 14 4 75.0% 77.8% 0.0026 0.0006 32 18 IL32 IL8 0.24 28 4 15 3 87.5% 83.3% 0.0337 0.0011 32 18 IL8 TLR2 0.24 27 5 15 3 84.4% 83.3% 0.0004 0.0388 32 18 ICAM1 TLR4 0.24 26 6 14 4 81.3% 77.8% 0.0003 0.0015 32 18 CD8A TOSO 0.24 24 7 14 4 77.4% 77.8% 0.0013 0.0002 31 18 ICAM1 IL8 0.24 27 5 15 3 84.4% 83.3% 0.0430 0.0016 32 18 CCL3 IL8 0.24 24 7 14 4 77.4% 77.8% 0.0356 0.0006 31 18 CASP1 LTA 0.24 24 8 14 4 75.0% 77.8% 0.0320 0.0007 32 18 IL10 IL1R1 0.23 24 8 14 4 75.0% 77.8% 0.0004 0.0038 32 18 IFNG IL8 0.23 25 7 14 4 78.1% 77.8% 0.0500 0.0002 32 18 PLAUR TNFRSF1A 0.23 24 8 14 4 75.0% 77.8% 0.0001 0.0093 32 18 DPP4 TLR2 0.23 24 8 14 4 75.0% 77.8% 0.0006 0.0220 32 18 LTA MNDA 0.23 25 7 14 4 78.1% 77.8% 0.0004 0.0405 32 18 HLADRA TNFSF5 0.23 25 7 14 4 78.1% 77.8% 0.0177 0.0007 32 18 TLR2 TNFSF5 0.23 25 7 14 4 78.1% 77.8% 0.0182 0.0007 32 18 CTLA4 MHC2TA 0.23 25 7 14 4 78.1% 77.8% 0.0041 0.0004 32 18 TIMP1 TNFSF5 0.23 25 7 14 4 78.1% 77.8% 0.0208 0.0007 32 18 IL18BP TXNRD1 0.22 24 7 14 4 77.4% 77.8% 0.0088 0.0006 31 18 DPP4 GZMB 0.22 24 8 14 4 75.0% 77.8% 0.0004 0.0348 32 18 HSPA1A TXNRD1 0.22 24 8 14 4 75.0% 77.8% 0.0136 0.0002 32 18 CASP3 TNFSF6 0.22 24 8 14 4 75.0% 77.8% 0.0003 0.0223 32 18 IL10 TLR4 0.22 27 5 15 3 84.4% 83.3% 0.0007 0.0071 32 18 IL10 PTPRC 0.21 25 7 14 4 78.1% 77.8% 0.0006 0.0083 32 18 PTGS2 TLR2 0.20 25 7 14 4 78.1% 77.8% 0.0018 0.0008 32 18 ICAM1 PTGS2 0.20 24 8 14 4 75.0% 77.8% 0.0008 0.0065 32 18 CCR5 IFI16 0.20 25 7 14 4 78.1% 77.8% 0.0233 0.0304 32 18 MIF PLAUR 0.19 25 7 14 4 78.1% 77.8% 0.0396 0.0011 32 18 ADAM17 MHC2TA 0.19 24 8 14 4 75.0% 77.8% 0.0154 0.0038 32 18 CD8A TXNRD1 0.19 24 8 14 4 75.0% 77.8% 0.0419 0.0009 32 18 IL1R1 IRF1 0.19 24 8 14 4 75.0% 77.8% 0.0078 0.0021 32 18 APAF1 CD86 0.19 26 6 15 3 81.3% 83.3% 0.0005 0.0294 32 18 ICAM1 MYC 0.17 24 8 14 4 75.0% 77.8% 0.0024 0.0211 32 18 IL32 TNFRSF13 0.17 24 8 14 4 75.0% 77.8% 0.0057 0.0186 32 18 MMP9 TLR4 0.16 25 7 14 4 78.1% 77.8% 0.0055 0.0025 32 18 HSPA1A IRF1 0.16 25 7 14 4 78.1% 77.8% 0.0243 0.0014 32 18 TIMP1 TOSO 0.16 24 7 14 4 77.4% 77.8% 0.0264 0.0128 31 18 IL18 SSI3 0.15 24 8 14 4 75.0% 77.8% 0.0177 0.0135 32 18 ALOX5 TLR4 0.15 24 8 14 4 75.0% 77.8% 0.0075 0.0028 32 18 HLADRA IL15 0.12 24 8 14 4 75.0% 77.8% 0.0211 0.0424 32 18

TABLE 2B Colon Normals Sum Group Size 36.0% 64.0% 100% N = 18 32 50  Gene Mean Mean p-val C1QA 19.1 20.9 4.9E−08 EGR1 18.7 19.7 3.9E−05 CCL5 11.6 12.2 0.0002 TNF 18.2 18.7 0.0003 HMOX1 16.0 16.6 0.0004 TGFB1 12.3 12.8 0.0005 IL8 22.3 21.3 0.0007 LTA 20.4 19.9 0.0010 DPP4 19.0 18.4 0.0016 TNFSF5 18.1 17.5 0.0021 CASP3 20.2 19.8 0.0025 PLAUR 14.6 15.0 0.0036 CCR5 17.2 17.7 0.0039 TXNRD1 17.3 16.8 0.0039 IFI16 15.2 16.0 0.0050 APAF1 17.2 16.7 0.0062 PLA2G7 19.7 19.0 0.0064 IL10 22.9 23.7 0.0084 MHC2TA 15.8 16.1 0.0095 ICAM1 16.8 17.2 0.0175 IL32 13.3 13.7 0.0216 IRF1 12.5 12.8 0.0219 TOSO 15.9 15.5 0.0241 SSI3 17.2 17.7 0.0343 ADAM17 18.7 18.4 0.0396 CASP1 15.6 15.9 0.0454 IL18 21.8 21.4 0.0457 HLADRA 12.0 12.2 0.0551 CCL3 19.8 20.2 0.0649 TLR2 15.8 16.1 0.0657 TIMP1 14.1 14.4 0.0726 TNFRSF13B 20.1 19.7 0.0752 IL1R1 20.6 20.2 0.0918 MNDA 12.4 12.7 0.1000 TLR4 15.1 14.8 0.1036 CTLA4 19.4 19.1 0.1046 IL18BP 16.7 17.0 0.1125 IL15 21.4 21.0 0.1153 PTPRC 11.8 11.6 0.1310 CD19 19.2 18.9 0.1402 MIF 15.5 15.3 0.1497 GZMB 15.9 16.4 0.1570 IL1RN 16.1 16.3 0.1705 IL23A 21.5 21.3 0.1820 MYC 18.2 18.0 0.1840 PTGS2 17.1 16.8 0.1845 IL1B 15.6 15.9 0.2112 TNFSF6 19.8 20.0 0.2370 CD8A 15.3 15.6 0.2424 IFNG 22.8 23.1 0.2554 MMP9 14.1 14.5 0.2737 NFKB1 16.8 16.7 0.2991 SERPINA1 12.5 12.7 0.3440 IL5 21.7 21.5 0.3448 CXCR3 17.2 17.4 0.3481 ALOX5 16.0 16.1 0.3599 CD4 15.3 15.2 0.4102 CXCL1 19.2 19.0 0.4253 CCR3 16.6 16.5 0.4941 TNFRSF1A 14.8 14.9 0.5094 SERPINE1 20.5 20.6 0.5422 CD86 17.9 17.9 0.5875 MAPK14 14.8 14.9 0.5938 ELA2 20.7 20.9 0.6264 VEGF 23.2 23.3 0.7141 HSPA1A 14.4 14.3 0.7476 MMP12 23.3 23.1 0.7872 HMGB1 16.9 16.9 0.9920

TABLE 2C Pre- dicted pro- bability of Colon Patient ID Group HMOX1 TXNRD1 logit odds Inf CC-019 Colon 16.02 18.00 8.34 4194.09 0.9998 CC-020 Colon 15.13 17.16 7.92 2748.70 0.9996 CC-003 Colon 16.03 17.77 6.62 747.28 0.9987 CC-014 Colon 15.84 17.50 5.82 336.20 0.9970 CC-004 Colon 16.20 17.59 4.26 71.14 0.9861 CC-018 Colon 15.49 16.95 4.15 63.21 0.9844 CC-002 Colon 15.68 17.04 3.58 35.72 0.9728 CC-005 Colon 16.59 17.79 3.16 23.58 0.9593 CC-011 Colon 15.12 16.48 3.06 21.39 0.9553 CC-007 Colon 16.46 17.60 2.63 13.87 0.9327 CC-006 Colon 16.22 17.38 2.54 12.71 0.9271 CC-012 Colon 16.05 17.16 2.05 7.74 0.8856 CC-008 Colon 16.07 17.17 2.03 7.65 0.8844 CC-009 Colon 16.47 17.45 1.45 4.28 0.8107 HN-003 Normals 15.71 16.69 0.88 2.42 0.7073 CC-001 Colon 15.06 16.11 0.82 2.26 0.6933 CC-013 Colon 16.93 17.70 0.37 1.44 0.5905 HN-001 Normals 16.73 17.49 0.14 1.15 0.5353 CC-015 Colon 16.57 17.33 0.01 1.01 0.5031 HN-020 Normals 16.11 16.82 −0.72 0.49 0.3267 HN-016 Normals 16.94 17.51 −1.10 0.33 0.2494 HN-010 Normals 16.62 17.21 −1.15 0.32 0.2397 HN-011 Normals 16.57 17.10 −1.65 0.19 0.1617 HN-004 Normals 15.07 15.77 −1.65 0.19 0.1615 HN-029 Normals 16.92 17.35 −2.14 0.12 0.1052 HN-022 Normals 17.98 18.19 −2.84 0.06 0.0550 HN-023 Normals 16.44 16.80 −3.03 0.05 0.0462 HN-032 Normals 16.47 16.80 −3.19 0.04 0.0394 HN-028 Normals 16.47 16.78 −3.34 0.04 0.0342 HN-027 Normals 16.76 17.02 −3.46 0.03 0.0305 HN-021 Normals 16.39 16.68 −3.53 0.03 0.0286 HN-026 Normals 16.41 16.69 −3.62 0.03 0.0260 HN-019 Normals 16.49 16.75 −3.65 0.03 0.0253 HN-018 Normals 16.25 16.52 −3.82 0.02 0.0215 HN-017 Normals 16.95 17.12 −3.98 0.02 0.0183 HN-031 Normals 16.79 16.95 −4.14 0.02 0.0157 HN-014 Normals 16.26 16.48 −4.17 0.02 0.0152 HN-009 Normals 16.59 16.75 −4.38 0.01 0.0124 HN-012 Normals 16.63 16.77 −4.39 0.01 0.0123 CC-010 Colon 16.46 16.61 −4.47 0.01 0.0114 HN-015 Normals 16.87 16.96 −4.61 0.01 0.0099 HN-007 Normals 16.29 16.44 −4.67 0.01 0.0093 HN-024 Normals 17.19 17.23 −4.69 0.01 0.0091 HN-002 Normals 17.18 17.21 −4.80 0.01 0.0082 HN-030 Normals 17.42 17.29 −5.72 0.00 0.0033 HN-006 Normals 16.94 16.82 −6.07 0.00 0.0023 HN-008 Normals 15.87 15.79 −6.61 0.00 0.0013 HN-005 Normals 16.50 16.24 −7.45 0.00 0.0006 HN-013 Normals 16.66 16.32 −7.89 0.00 0.0004 HN-025 Normals 17.06 16.53 −8.91 0.00 0.0001

TABLE 3A total used Normal Colon (excludes En- N = 50 23 missing) 2-gene models and tropy #normal #normal #cc #cc Correct Correct # # 1-gene models R-sq Correct FALSE Correct FALSE Classification Classification p-val 1 p-val 2 normals disease ATM CDKN2A 0.64 44 6 21 2 88.0% 91.3% 4.2E−07 2.8E−08 50 23 CDK4 CDKN2A 0.62 47 3 21 2 94.0% 91.3% 1.1E−06 2.2E−13 50 23 CDKN2A ITGB1 0.62 47 3 21 2 94.0% 91.3% 7.0E−12 1.2E−06 50 23 CDKN2A TNFRSF10A 0.62 46 4 20 3 92.0% 87.0% 1.9E−11 1.3E−06 50 23 RHOC SMAD4 0.58 44 6 20 3 88.0% 87.0% 1.3E−09 1.6E−07 50 23 ATM GZMA 0.58 43 7 20 3 86.0% 87.0% 8.3E−11 5.0E−07 50 23 CDK4 RHOC 0.56 43 7 20 3 86.0% 87.0% 4.3E−07 3.7E−12 50 23 ATM RHOC 0.56 43 7 20 3 86.0% 87.0% 5.1E−07 1.5E−06 50 23 CDKN2A ITGAE 0.56 45 5 21 2 90.0% 91.3% 1.5E−09 2.5E−05 50 23 CDKN2A MSH2 0.56 42 8 20 3 84.0% 87.0% 5.4E−07 2.6E−05 50 23 EGR1 NME4 0.54 44 6 20 3 88.0% 87.0% 2.6E−11 1.7E−07 50 23 RHOC VHL 0.54 47 3 21 2 94.0% 91.3% 1.1E−11 1.4E−06 50 23 CDKN2A ITGA3 0.54 42 7 19 4 85.7% 82.6% 7.8E−12 8.1E−05 49 23 ITGAE RHOC 0.54 43 7 20 3 86.0% 87.0% 1.5E−06 4.1E−09 50 23 BCL2 CDKN2A 0.53 46 4 20 3 92.0% 87.0% 9.6E−05 1.8E−11 50 23 CDKN2A SMAD4 0.52 44 6 20 3 88.0% 87.0% 2.4E−08 0.0002 50 23 SMAD4 TNF 0.52 42 8 20 3 84.0% 87.0% 1.8E−07 2.6E−08 50 23 CDKN2A PTCH1 0.51 43 7 20 3 86.0% 87.0% 1.3E−11 0.0002 50 23 ATM TNF 0.51 44 6 20 3 88.0% 87.0% 2.4E−07 1.3E−05 50 23 CDKN2A COL18A1 0.51 45 5 20 3 90.0% 87.0% 2.3E−11 0.0002 50 23 BCL2 RHOC 0.50 40 10 20 3 80.0% 87.0% 6.5E−06 5.6E−11 50 23 ATM NRAS 0.50 45 5 19 4 90.0% 82.6% 1.5E−10 2.2E−05 50 23 CDKN2A ERBB2 0.50 41 9 19 4 82.0% 82.6% 4.7E−11 0.0004 50 23 NRAS SMAD4 0.50 43 7 20 3 86.0% 87.0% 6.9E−08 1.8E−10 50 23 CDKN2A HRAS 0.49 41 9 19 4 82.0% 82.6% 4.0E−11 0.0007 50 23 RHOC TNFRSF10A 0.48 40 10 18 5 80.0% 78.3% 1.0E−08 1.7E−05 50 23 MSH2 RHOC 0.48 43 7 20 3 86.0% 87.0% 2.1E−05 2.0E−05 50 23 CDKN2A SKIL 0.48 43 7 20 3 86.0% 87.0% 5.9E−07 0.0011 50 23 ATM PCNA 0.48 44 6 20 3 88.0% 87.0% 4.2E−11 7.0E−05 50 23 NFKB1 RHOC 0.47 42 8 20 3 84.0% 87.0% 3.7E−05 1.5E−10 50 23 RHOC TP53 0.47 42 8 20 3 84.0% 87.0% 7.7E−11 4.0E−05 50 23 CDKN2A SKI 0.47 40 10 19 4 80.0% 82.6% 4.2E−10 0.0021 50 23 CDKN2A EGR1 0.46 39 11 19 4 78.0% 82.6% 6.5E−06 0.0024 50 23 CDKN2A IFITM1 0.46 42 8 20 3 84.0% 87.0% 4.1E−08 0.0028 50 23 CDKN2A VHL 0.46 41 9 20 3 82.0% 87.0% 4.3E−10 0.0029 50 23 CDKN2A IL8 0.46 39 11 19 4 78.0% 82.6% 1.3E−07 0.0029 50 23 CDKN2A NME4 0.46 44 6 19 4 88.0% 82.6% 1.2E−09 0.0032 50 23 CDKN2A NFKB1 0.46 42 8 19 4 84.0% 82.6% 2.6E−10 0.0034 50 23 SMAD4 TIMP1 0.45 39 11 18 5 78.0% 78.3% 4.9E−09 5.3E−07 50 23 CDK2 CDKN2A 0.45 42 8 19 4 84.0% 82.6% 0.0041 1.4E−10 50 23 ITGB1 RHOC 0.45 41 9 19 4 82.0% 82.6% 7.9E−05 1.8E−08 50 23 CASP8 CDKN2A 0.45 40 10 19 4 80.0% 82.6% 0.0050 1.5E−09 50 23 CDKN2A TP53 0.45 40 10 19 4 80.0% 82.6% 1.8E−10 0.0051 50 23 PTCH1 RHOC 0.45 42 8 19 4 84.0% 82.6% 0.0001 3.3E−10 50 23 ERBB2 RHOC 0.44 39 11 19 4 78.0% 82.6% 0.0001 6.8E−10 50 23 NME4 RHOC 0.44 42 8 19 4 84.0% 82.6% 0.0001 2.4E−09 50 23 ITGA3 RHOC 0.44 41 8 19 4 83.7% 82.6% 0.0001 6.7E−10 49 23 ITGAE TNF 0.44 40 10 18 5 80.0% 78.3% 7.7E−06 3.7E−07 50 23 CDKN2A MYC 0.44 38 12 19 4 76.0% 82.6% 6.6E−10 0.0086 50 23 CDKN2A PCNA 0.44 42 8 20 3 84.0% 87.0% 3.1E−10 0.0097 50 23 APAF1 CDKN2A 0.43 41 9 19 4 82.0% 82.6% 0.0101 7.5E−08 50 23 MSH2 NME4 0.43 41 9 19 4 82.0% 82.6% 4.0E−09 0.0002 50 23 GZMA MSH2 0.43 43 7 19 4 86.0% 82.6% 0.0002 1.0E−07 50 23 RHOC SRC 0.43 42 8 20 3 84.0% 87.0% 8.9E−10 0.0003 50 23 AKT1 RHOC 0.42 41 9 18 5 82.0% 78.3% 0.0003 6.3E−10 50 23 CDKN2A FOS 0.42 39 10 18 5 79.6% 78.3% 2.1E−08 0.0205 49 23 CDKN2A NME1 0.42 44 6 19 4 88.0% 82.6% 6.5E−10 0.0225 50 23 ATM WNT1 0.42 43 7 19 4 86.0% 82.6% 8.4E−09 0.0012 50 23 RHOC SKI 0.42 39 11 18 5 78.0% 78.3% 3.9E−09 0.0004 50 23 MYCL1 RHOC 0.42 41 9 19 4 82.0% 82.6% 0.0004 7.7E−10 50 23 ITGB1 TNF 0.41 39 11 19 4 78.0% 82.6% 2.9E−05 1.3E−07 50 23 ATM TGFB1 0.41 42 8 20 3 84.0% 87.0% 6.0E−08 0.0020 50 23 ABL2 RHOC 0.41 41 9 18 5 82.0% 78.3% 0.0007 1.4E−09 50 23 HRAS RHOC 0.41 42 8 19 4 84.0% 82.6% 0.0007 1.6E−09 50 23 MYC RHOC 0.41 42 8 19 4 84.0% 82.6% 0.0007 2.7E−09 50 23 AKT1 CDKN2A 0.41 40 10 19 4 80.0% 82.6% 0.0441 1.4E−09 50 23 CDKN2A E2F1 0.41 42 8 19 4 84.0% 82.6% 3.6E−06 0.0453 50 23 CDKN2A IL18 0.40 42 8 19 4 84.0% 82.6% 1.9E−08 0.0491 50 23 RHOC SKIL 0.40 45 5 20 3 90.0% 87.0% 2.1E−05 0.0008 50 23 ABL1 CDKN2A 0.40 41 9 18 5 82.0% 78.3% 0.0500 1.8E−09 50 23 MSH2 PCNA 0.40 39 11 18 5 78.0% 78.3% 1.4E−09 0.0008 50 23 EGR1 RHOC 0.40 38 12 18 5 76.0% 78.3% 0.0009 0.0001 50 23 ATM TIMP1 0.40 42 8 18 5 84.0% 78.3% 6.0E−08 0.0030 50 23 TNF TNFRSF10A 0.40 39 11 19 4 78.0% 82.6% 5.3E−07 5.0E−05 50 23 EGR1 ITGAE 0.40 42 8 19 4 84.0% 82.6% 2.6E−06 0.0001 50 23 GZMA SMAD4 0.40 45 5 18 5 90.0% 78.3% 7.9E−06 4.6E−07 50 23 MSH2 TNF 0.40 42 8 20 3 84.0% 87.0% 6.1E−05 0.0012 50 23 ATM BAX 0.40 38 12 19 4 76.0% 82.6% 2.6E−09 0.0039 50 23 TNF VHL 0.39 42 8 18 5 84.0% 78.3% 9.3E−09 6.8E−05 50 23 ATM IFNG 0.39 40 10 19 4 80.0% 82.6% 2.7E−09 0.0044 50 23 ATM BAD 0.39 42 8 19 4 84.0% 82.6% 3.8E−09 0.0048 50 23 NOTCH2 RHOC 0.39 43 7 18 5 86.0% 78.3% 0.0015 2.7E−09 50 23 SKIL TNFRSF6 0.39 42 8 19 4 84.0% 82.6% 2.6E−09 4.3E−05 50 23 EGR1 GZMA 0.39 42 8 20 3 84.0% 87.0% 7.9E−07 0.0003 50 23 GZMA SKIL 0.39 39 11 19 4 78.0% 82.6% 5.1E−05 8.0E−07 50 23 SKI TGFB1 0.38 39 11 19 4 78.0% 82.6% 2.0E−07 2.0E−08 50 23 NFKB1 TNF 0.38 40 10 18 5 80.0% 78.3% 0.0001 8.1E−09 50 23 RHOC SEMA4D 0.38 40 10 18 5 80.0% 78.3% 4.9E−09 0.0027 50 23 RHOC TNFRSF10B 0.38 39 11 18 5 78.0% 78.3% 9.3E−09 0.0027 50 23 MSH2 TGFB1 0.38 43 7 19 4 86.0% 82.6% 2.4E−07 0.0027 50 23 ATM EGR1 0.38 41 9 19 4 82.0% 82.6% 0.0004 0.0095 50 23 ATM TP53 0.38 39 11 18 5 78.0% 78.3% 5.0E−09 0.0098 50 23 ITGAE TGFB1 0.38 38 12 18 5 76.0% 78.3% 2.7E−07 7.1E−06 50 23 CASP8 RHOC 0.38 40 10 18 5 80.0% 78.3% 0.0033 4.5E−08 50 23 ATM ITGA1 0.37 38 12 18 5 76.0% 78.3% 8.2E−09 0.0127 50 23 ATM NME4 0.37 40 10 19 4 80.0% 82.6% 7.2E−08 0.0145 50 23 ATM TNFRSF6 0.37 40 10 18 5 80.0% 78.3% 6.6E−09 0.0145 50 23 RHOA RHOC 0.37 40 10 18 5 80.0% 78.3% 0.0050 8.2E−09 50 23 CDK4 TNF 0.37 38 12 18 5 76.0% 78.3% 0.0002 3.3E−08 50 23 BCL2 TNF 0.37 38 12 18 5 76.0% 78.3% 0.0003 3.6E−08 50 23 APAF1 RHOC 0.37 41 9 19 4 82.0% 82.6% 0.0056 2.0E−06 50 23 ATM PLAUR 0.37 40 9 19 4 81.6% 82.6% 5.2E−08 0.0145 49 23 ATM IFITM1 0.36 39 11 18 5 78.0% 78.3% 3.8E−06 0.0193 50 23 CDK5 SMAD4 0.36 45 5 18 5 90.0% 78.3% 4.0E−05 1.9E−08 50 23 FOS RHOC 0.36 38 11 19 4 77.6% 82.6% 0.0156 3.4E−07 49 23 SKIL TNF 0.36 41 9 19 4 82.0% 82.6% 0.0003 0.0002 50 23 RHOA SMAD4 0.36 41 9 18 5 82.0% 78.3% 4.1E−05 1.0E−08 50 23 ATM TNFRSF1A 0.36 44 6 18 5 88.0% 78.3% 4.4E−08 0.0208 50 23 ABL1 RHOC 0.36 42 8 19 4 84.0% 82.6% 0.0065 1.3E−08 50 23 ABL1 ATM 0.36 42 8 18 5 84.0% 78.3% 0.0215 1.3E−08 50 23 ATM IGFBP3 0.36 40 10 18 5 80.0% 78.3% 1.6E−08 0.0218 50 23 CDKN2A 0.36 40 10 18 5 80.0% 78.3% 9.5E−09 50 23 NME4 TNF 0.36 40 10 18 5 80.0% 78.3% 0.0003 1.1E−07 50 23 COL18A1 RHOC 0.36 39 11 19 4 78.0% 82.6% 0.0073 2.6E−08 50 23 SMAD4 TNFRSF1A 0.36 40 10 18 5 80.0% 78.3% 5.3E−08 5.0E−05 50 23 ATM ITGAE 0.36 38 12 18 5 76.0% 78.3% 1.7E−05 0.0261 50 23 NRAS SKIL 0.36 44 6 19 4 88.0% 82.6% 0.0002 1.3E−07 50 23 BRCA1 RHOC 0.36 39 11 18 5 78.0% 78.3% 0.0094 8.7E−08 50 23 GZMA ITGB1 0.35 40 10 18 5 80.0% 78.3% 2.0E−06 3.7E−06 50 23 ATM FOS 0.35 38 11 18 5 77.6% 78.3% 5.8E−07 0.0340 49 23 EGR1 SMAD4 0.35 41 9 18 5 82.0% 78.3% 7.1E−05 0.0014 50 23 MSH2 NRAS 0.35 39 11 19 4 78.0% 82.6% 1.9E−07 0.0122 50 23 IFITM1 SKIL 0.35 41 9 18 5 82.0% 78.3% 0.0003 7.7E−06 50 23 BAX MSH2 0.35 38 12 19 4 76.0% 82.6% 0.0125 2.3E−08 50 23 ATM RHOA 0.35 38 12 18 5 76.0% 78.3% 2.0E−08 0.0449 50 23 ATM PTCH1 0.35 40 10 18 5 80.0% 78.3% 3.0E−08 0.0450 50 23 MSH2 TIMP1 0.35 41 9 19 4 82.0% 82.6% 7.4E−07 0.0134 50 23 ATM RB1 0.35 39 11 18 5 78.0% 78.3% 2.1E−07 0.0468 50 23 ATM IL8 0.35 39 11 18 5 78.0% 78.3% 2.7E−05 0.0476 50 23 SKIL TIMP1 0.35 42 8 19 4 84.0% 82.6% 7.9E−07 0.0003 50 23 CDK5 RHOC 0.35 41 9 19 4 82.0% 82.6% 0.0152 4.3E−08 50 23 CFLAR RHOC 0.34 40 10 18 5 80.0% 78.3% 0.0167 2.8E−07 50 23 ITGAE TIMP1 0.34 39 11 18 5 78.0% 78.3% 8.8E−07 3.4E−05 50 23 BAX RHOC 0.34 42 8 18 5 84.0% 78.3% 0.0168 2.9E−08 50 23 TNF TP53 0.34 40 10 18 5 80.0% 78.3% 2.5E−08 0.0008 50 23 ITGAE MSH2 0.34 44 6 18 5 88.0% 78.3% 0.0175 3.7E−05 50 23 MSH2 NME1 0.34 39 11 18 5 78.0% 78.3% 2.4E−08 0.0177 50 23 MSH2 WNT1 0.34 42 8 19 4 84.0% 82.6% 3.1E−07 0.0178 50 23 SMAD4 WNT1 0.34 41 9 19 4 82.0% 82.6% 3.3E−07 0.0001 50 23 MSH2 S100A4 0.34 41 9 19 4 82.0% 82.6% 3.4E−08 0.0191 50 23 RB1 RHOC 0.34 41 9 19 4 82.0% 82.6% 0.0208 3.0E−07 50 23 ITGB1 NRAS 0.34 42 8 18 5 84.0% 78.3% 3.2E−07 4.0E−06 50 23 IFITM1 MSH2 0.34 40 10 18 5 80.0% 78.3% 0.0230 1.4E−05 50 23 E2F1 RHOC 0.34 39 11 18 5 78.0% 78.3% 0.0247 9.9E−05 50 23 CDK5 MSH2 0.34 44 6 19 4 88.0% 82.6% 0.0246 6.9E−08 50 23 EGR1 MSH2 0.34 39 11 19 4 78.0% 82.6% 0.0251 0.0031 50 23 BAD MSH2 0.34 40 10 18 5 80.0% 78.3% 0.0256 5.4E−08 50 23 APAF1 IFITM1 0.33 39 11 18 5 78.0% 78.3% 1.6E−05 9.0E−06 50 23 IL8 RHOC 0.33 40 10 18 5 80.0% 78.3% 0.0301 5.4E−05 50 23 APAF1 TNF 0.33 38 12 18 5 76.0% 78.3% 0.0014 1.0E−05 50 23 BRAF RHOC 0.33 40 10 18 5 80.0% 78.3% 0.0340 1.2E−07 50 23 ABL2 SMAD4 0.33 40 10 19 4 80.0% 82.6% 0.0002 5.9E−08 50 23 MSH2 PLAUR 0.33 37 12 18 5 75.5% 78.3% 3.1E−07 0.0299 49 23 GZMA RHOC 0.33 42 8 19 4 84.0% 82.6% 0.0434 1.4E−05 50 23 FOS MSH2 0.32 40 9 19 4 81.6% 82.6% 0.0436 2.1E−06 49 23 IL8 MSH2 0.32 39 11 18 5 78.0% 78.3% 0.0448 8.0E−05 50 23 EGR1 SKIL 0.32 41 9 18 5 82.0% 78.3% 0.0011 0.0057 50 23 NME4 SKIL 0.32 39 11 18 5 78.0% 78.3% 0.0012 7.0E−07 50 23 E2F1 ITGAE 0.32 39 11 18 5 78.0% 78.3% 0.0001 0.0002 50 23 E2F1 GZMA 0.32 39 11 18 5 78.0% 78.3% 2.2E−05 0.0003 50 23 APAF1 FOS 0.31 38 11 18 5 77.6% 78.3% 3.5E−06 2.0E−05 49 23 BRAF TNF 0.31 41 9 18 5 82.0% 78.3% 0.0035 2.7E−07 50 23 GZMA IL8 0.31 40 10 18 5 80.0% 78.3% 0.0002 2.8E−05 50 23 SKIL TGFB1 0.31 41 9 18 5 82.0% 78.3% 6.6E−06 0.0021 50 23 FOS SKIL 0.31 40 9 18 5 81.6% 78.3% 0.0018 4.7E−06 49 23 TGFB1 TNFRSF10A 0.30 40 10 18 5 80.0% 78.3% 5.0E−05 8.5E−06 50 23 IL1B SKIL 0.30 42 8 18 5 84.0% 78.3% 0.0032 2.9E−07 50 23 SEMA4D TNF 0.30 42 8 18 5 84.0% 78.3% 0.0073 2.3E−07 50 23 APAF1 EGR1 0.30 40 10 18 5 80.0% 78.3% 0.0211 5.0E−05 50 23 SKIL TNFRSF1A 0.30 42 8 18 5 84.0% 78.3% 9.4E−07 0.0038 50 23 APAF1 TGFB1 0.30 40 10 19 4 80.0% 82.6% 1.3E−05 5.4E−05 50 23 EGR1 SKI 0.29 40 10 19 4 80.0% 82.6% 1.3E−06 0.0247 50 23 PLAUR SKIL 0.29 38 11 18 5 77.6% 78.3% 0.0038 1.5E−06 49 23 IL8 TNF 0.29 39 11 18 5 78.0% 78.3% 0.0105 0.0004 50 23 CDK5 SKIL 0.29 38 12 18 5 76.0% 78.3% 0.0057 6.1E−07 50 23 EGR1 MYC 0.29 38 12 18 5 76.0% 78.3% 7.6E−07 0.0363 50 23 BAD SMAD4 0.29 39 11 18 5 78.0% 78.3% 0.0017 5.4E−07 50 23 COL18A1 EGR1 0.29 40 10 18 5 80.0% 78.3% 0.0390 8.5E−07 50 23 PCNA SMAD4 0.29 42 8 19 4 84.0% 82.6% 0.0017 3.4E−07 50 23 GZMA IFITM1 0.29 41 9 18 5 82.0% 78.3% 0.0002 9.4E−05 50 23 CFLAR TNF 0.29 39 11 18 5 78.0% 78.3% 0.0141 4.5E−06 50 23 BCL2 EGR1 0.28 41 9 18 5 82.0% 78.3% 0.0434 1.7E−06 50 23 MMP9 SKIL 0.28 41 9 19 4 82.0% 82.6% 0.0084 1.9E−06 50 23 RHOC 0.28 38 12 18 5 76.0% 78.3% 4.2E−07 50 23 E2F1 TNF 0.28 38 12 18 5 76.0% 78.3% 0.0178 0.0015 50 23 MSH2 0.28 41 9 19 4 82.0% 82.6% 4.4E−07 50 23 BAX TNFRSF10A 0.28 38 12 18 5 76.0% 78.3% 0.0002 7.4E−07 50 23 NRAS VHL 0.28 39 11 18 5 78.0% 78.3% 2.5E−06 6.4E−06 50 23 NRAS TNFRSF10A 0.27 40 10 18 5 80.0% 78.3% 0.0002 6.6E−06 50 23 ITGA1 SKIL 0.27 39 11 18 5 78.0% 78.3% 0.0126 8.4E−07 50 23 IFITM1 ITGAE 0.27 40 10 18 5 80.0% 78.3% 0.0011 0.0003 50 23 PCNA SKIL 0.27 45 5 18 5 90.0% 78.3% 0.0176 8.1E−07 50 23 ITGAE PLAUR 0.27 38 11 18 5 77.6% 78.3% 5.6E−06 0.0013 49 23 ABL1 SMAD4 0.26 40 10 18 5 80.0% 78.3% 0.0053 1.3E−06 50 23 BAX ITGAE 0.26 39 11 18 5 78.0% 78.3% 0.0017 1.3E−06 50 23 SERPINE1 SKIL 0.26 38 12 18 5 76.0% 78.3% 0.0269 2.8E−05 50 23 NOTCH2 SMAD4 0.26 39 11 18 5 78.0% 78.3% 0.0069 1.4E−06 50 23 BAX SMAD4 0.26 43 7 19 4 86.0% 82.6% 0.0072 1.7E−06 50 23 BAD ITGAE 0.26 40 10 18 5 80.0% 78.3% 0.0024 2.2E−06 50 23 ITGAE WNT1 0.25 38 12 18 5 76.0% 78.3% 2.0E−05 0.0027 50 23 CFLAR TGFB1 0.25 38 12 18 5 76.0% 78.3% 0.0001 2.6E−05 50 23 CDK2 SMAD4 0.24 39 11 18 5 78.0% 78.3% 0.0139 2.4E−06 50 23 S100A4 SMAD4 0.24 40 10 18 5 80.0% 78.3% 0.0151 3.4E−06 50 23 FOS PTEN 0.24 38 11 18 5 77.6% 78.3% 8.0E−05 0.0001 49 23 ITGB1 WNT1 0.24 38 12 18 5 76.0% 78.3% 3.6E−05 0.0004 50 23 EGR1 0.24 39 11 18 5 78.0% 78.3% 3.0E−06 50 23 FOS IL8 0.24 38 11 18 5 77.6% 78.3% 0.0071 0.0001 49 23 ITGAE SMAD4 0.24 40 10 18 5 80.0% 78.3% 0.0224 0.0071 50 23 CDK4 TGFB1 0.23 38 12 18 5 76.0% 78.3% 0.0003 2.1E−05 50 23 BAD TNFRSF10A 0.23 38 12 18 5 76.0% 78.3% 0.0018 7.6E−06 50 23 CDKN1A NME4 0.23 40 10 18 5 80.0% 78.3% 6.2E−05 0.0001 50 23 IFITM1 TNFRSF10A 0.22 38 12 18 5 76.0% 78.3% 0.0025 0.0033 50 23 ABL2 TNFRSF10A 0.22 40 10 18 5 80.0% 78.3% 0.0025 8.3E−06 50 23

TABLE 3B Colon Normals Sum Group Size 31.5% 68.5% 100% N = 23 50 73  Gene Mean Mean p-val CDKN2A 20.1 21.1 9.5E−09 ATM 17.3 16.5 1.4E−07 RHOC 15.9 16.6 4.2E−07 MSH2 18.7 17.9 4.4E−07 EGR1 18.9 19.8 3.0E−06 TNF 18.1 18.7 8.0E−06 SKIL 18.6 17.8 1.5E−05 SMAD4 17.3 16.9 5.7E−05 E2F1 19.5 20.2 8.4E−05 ITGAE 24.3 23.3 0.0002 IL8 22.3 21.4 0.0002 IFITM1 8.4 9.0 0.0006 TNFRSF10A 21.2 20.7 0.0008 GZMA 17.3 17.8 0.0010 APAF1 17.5 17.0 0.0011 ITGB1 14.9 14.5 0.0020 TGFB1 12.4 12.7 0.0050 TIMP1 14.1 14.5 0.0076 PTEN 14.2 13.8 0.0088 FOS 15.1 15.6 0.0091 SERPINE1 20.6 21.1 0.0139 SOCS1 16.4 16.8 0.0139 CDKN1A 15.9 16.3 0.0149 ANGPT1 21.1 20.6 0.0172 IL18 22.1 21.7 0.0226 WNT1 21.2 21.6 0.0258 CFLAR 14.9 14.6 0.0262 NRAS 16.8 17.0 0.0309 RB1 17.8 17.5 0.0310 NME4 17.6 17.3 0.0313 CASP8 15.2 15.0 0.0380 BRCA1 21.6 21.3 0.0548 SKI 17.5 17.2 0.0638 PLAUR 14.6 14.9 0.0695 ICAM1 16.8 17.0 0.0697 TNFRSF1A 15.1 15.4 0.0809 BCL2 17.3 17.1 0.0859 MMP9 14.1 14.6 0.0877 CDK4 17.8 17.6 0.0890 VHL 17.4 17.2 0.0929 CDC25A 22.7 23.1 0.1161 ERBB2 22.6 22.4 0.1360 BRAF 16.9 16.7 0.1511 G1P3 15.1 15.4 0.1615 COL18A1 23.8 23.3 0.1790 CCNE1 22.8 23.1 0.1892 MYC 18.3 18.1 0.1898 ITGA3 22.0 21.8 0.2006 TNFRSF10B 17.2 17.0 0.2062 NFKB1 16.8 16.7 0.2158 CDK5 18.5 18.6 0.2245 RAF1 14.5 14.3 0.2450 THBS1 17.1 17.4 0.2556 SRC 18.1 18.3 0.2746 IL1B 15.6 15.8 0.2977 PTCH1 20.1 19.9 0.3142 IGFBP3 22.1 22.4 0.3151 BAD 18.1 18.2 0.3319 HRAS 20.2 20.0 0.3962 ITGA1 21.0 21.1 0.4121 FGFR2 22.5 22.8 0.4215 ABL1 18.1 18.2 0.4378 S100A4 13.0 13.2 0.4606 ABL2 20.1 20.2 0.4676 BAX 15.6 15.7 0.4717 IFNG 23.1 23.3 0.5189 SEMA4D 14.3 14.2 0.5559 AKT1 15.1 15.0 0.5652 PLAU 23.9 24.0 0.6255 RHOA 11.6 11.6 0.6256 NOTCH2 16.0 15.9 0.6295 TP53 16.3 16.2 0.7109 MYCL1 18.5 18.6 0.7168 JUN 20.9 20.9 0.8098 CDK2 19.2 19.2 0.8892 VEGF 22.7 22.8 0.9203 TNFRSF6 16.4 16.4 0.9420 NME1 19.3 19.3 0.9578 PCNA 18.1 18.1 0.9609

TABLE 3C Predicted probability Patient ID Group ATM CDKN2A logit odds of colon cancer CC-035 Colon Cancer 19.12 20.14 11.66 1.2E+05 1.0000 CC-020 Colon Cancer 18.09 19.23 9.86 1.9E+04 0.9999 CC-019 Colon Cancer 18.11 19.40 9.39 1.2E+04 0.9999 CC-005 Colon Cancer 17.88 19.87 6.71 8.2E+02 0.9988 CC-014 Colon Cancer 18.04 20.26 6.14 4.7E+02 0.9979 CC-004 Colon Cancer 17.38 19.40 5.95 3.8E+02 0.9974 CC-031 Colon Cancer 16.78 19.26 3.60 3.7E+01 0.9734 CC-013 Colon Cancer 17.61 20.60 2.98 2.0E+01 0.9516 CC-034 Colon Cancer 16.87 19.64 2.77 1.6E+01 0.9413 CC-007 Colon Cancer 17.45 20.48 2.64 1.4E+01 0.9337 CC-018 Colon Cancer 16.35 19.03 2.35 1.0E+01 0.9129 CC-006 Colon Cancer 17.11 20.13 2.25 9.4E+00 0.9043 CC-003 Colon Cancer 17.35 20.48 2.19 9.0E+00 0.8997 CC-032 Colon Cancer 16.98 19.96 2.16 8.6E+00 0.8963 CC-009 Colon Cancer 16.64 19.60 1.79 6.0E+00 0.8575 CC-012 Colon Cancer 17.18 20.41 1.62 5.1E+00 0.8353 HN-040 Normal 17.42 20.77 1.56 4.8E+00 0.8269 HN-049 Normal 17.05 20.42 0.97 2.6E+00 0.7244 CC-011 Colon Cancer 16.60 19.80 0.94 2.6E+00 0.7190 HN-035 Normal 16.61 19.82 0.93 2.5E+00 0.7166 CC-002 Colon Cancer 17.03 20.52 0.52 1.7E+00 0.6264 CC-008 Colon Cancer 17.30 20.94 0.43 1.5E+00 0.6051 CC-010 Colon Cancer 17.49 21.31 0.07 1.1E+00 0.5168 HN-041 Normal 16.70 20.26 −0.12 8.9E−01 0.4711 HN-016 Normal 17.14 21.12 −0.96 3.8E−01 0.2773 HN-012 Normal 16.28 19.97 −1.14 3.2E−01 0.2426 CC-033 Colon Cancer 16.39 20.15 −1.22 3.0E−01 0.2285 HN-019 Normal 16.72 20.66 −1.41 2.4E−01 0.1959 HN-014 Normal 16.79 20.82 −1.59 2.0E−01 0.1697 CC-015 Colon Cancer 16.73 20.76 −1.70 1.8E−01 0.1549 HN-050 Normal 16.38 20.33 −1.87 1.5E−01 0.1335 HN-104 Normal 16.39 20.36 −1.91 1.5E−01 0.1286 HN-001 Normal 17.04 21.30 −2.02 1.3E−01 0.1173 HN-005 Normal 16.22 20.17 −2.06 1.3E−01 0.1133 HN-039 Normal 16.63 20.76 −2.13 1.2E−01 0.1058 HN-004 Normal 16.55 20.65 −2.15 1.2E−01 0.1045 HN-030 Normal 16.82 21.05 −2.25 1.1E−01 0.0956 CC-001 Colon Cancer 16.53 20.74 −2.53 8.0E−02 0.0738 HN-036 Normal 16.76 21.12 −2.72 6.6E−02 0.0619 HN-020 Normal 16.59 20.94 −2.93 5.4E−02 0.0509 HN-047 Normal 16.43 20.72 −2.97 5.2E−02 0.0490 HN-007 Normal 16.18 20.46 −3.22 4.0E−02 0.0383 HN-034 Normal 16.73 21.22 −3.23 4.0E−02 0.0382 HN-029 Normal 17.15 21.83 −3.28 3.8E−02 0.0363 HN-038 Normal 16.47 20.88 −3.28 3.8E−02 0.0363 HN-106 Normal 16.09 20.34 −3.28 3.8E−02 0.0362 HN-045 Normal 16.35 20.79 −3.55 2.9E−02 0.0280 HN-101 Normal 16.11 20.46 −3.57 2.8E−02 0.0274 HN-044 Normal 16.24 20.66 −3.61 2.7E−02 0.0264 HN-002 Normal 17.32 22.28 −4.01 1.8E−02 0.0179 HN-003 Normal 16.73 21.51 −4.16 1.6E−02 0.0153 HN-022 Normal 17.26 22.31 −4.39 1.2E−02 0.0122 HN-013 Normal 16.48 21.24 −4.44 1.2E−02 0.0116 HN-028 Normal 16.12 20.79 −4.63 9.8E−03 0.0097 HN-107 Normal 16.48 21.36 −4.85 7.8E−03 0.0078 HN-032 Normal 16.37 21.24 −4.95 7.1E−03 0.0070 HN-037 Normal 16.83 21.92 −5.09 6.1E−03 0.0061 HN-010 Normal 15.87 20.59 −5.15 5.8E−03 0.0058 HN-024 Normal 16.54 21.60 −5.34 4.8E−03 0.0048 HN-102 Normal 16.03 20.91 −5.47 4.2E−03 0.0042 HN-026 Normal 16.62 21.77 −5.54 3.9E−03 0.0039 HN-008 Normal 15.93 20.89 −5.85 2.9E−03 0.0029 HN-009 Normal 16.36 21.57 −6.10 2.2E−03 0.0022 HN-103 Normal 15.65 20.59 −6.17 2.1E−03 0.0021 HN-027 Normal 16.17 21.37 −6.33 1.8E−03 0.0018 HN-015 Normal 16.47 21.80 −6.35 1.7E−03 0.0017 HN-025 Normal 16.09 21.46 −7.02 8.9E−04 0.0009 HN-105 Normal 16.21 21.67 −7.16 7.8E−04 0.0008 HN-042 Normal 15.94 21.36 −7.36 6.3E−04 0.0006 HN-017 Normal 16.74 22.53 −7.53 5.4E−04 0.0005 HN-018 Normal 16.46 22.16 −7.61 4.9E−04 0.0005 HN-033 Normal 17.15 23.74 −9.65 6.4E−05 0.0001 HN-021 Normal 16.07 22.74 −11.39 1.1E−05 0.0000

TABLE 4A Normal Colon total used N = 50 22 (excludes missing) Entropy #normal #normal #cc #cc Correct Correct # 2-gene models R-sq Correct FALSE Correct FALSE Classification Classification p-val 1 p-val 2 # normals disease NAB2 TGFB1 0.45 41 9 18 4 82.0% 81.8% 6.4E−09 4.6E−07 50 22 MAP2K1 TGFB1 0.45 44 6 18 4 88.0% 81.8% 7.6E−09 1.5E−09 50 22 TGFB1 TOPBP1 0.42 38 12 18 4 76.0% 81.8% 2.1E−06 2.9E−08 50 22 ICAM1 TOPBP1 0.30 41 9 18 4 82.0% 81.8% 0.0007 1.1E−06 50 22 CEBPB TOPBP1 0.29 39 11 17 5 78.0% 77.3% 0.0011 9.6E−07 50 22 EGR1 NAB2 0.28 41 9 18 4 82.0% 81.8% 0.0016 0.0002 50 22 NR4A2 TGFB1 0.27 40 10 17 5 80.0% 77.3% 2.8E−05 7.3E−05 50 22 NAB2 PDGFA 0.27 39 11 17 5 78.0% 77.3% 6.4E−06 0.0025 50 22 CREBBP TOPBP1 0.27 41 9 17 5 82.0% 77.3% 0.0026 1.3E−06 50 22 FOS NR4A2 0.26 38 11 17 5 77.6% 77.3% 0.0001 4.7E−05 49 22 NAB1 TGFB1 0.26 40 10 17 5 80.0% 77.3% 4.5E−05 0.0002 50 22 EGR1 NR4A2 0.26 39 11 17 5 78.0% 77.3% 0.0001 0.0004 50 22 TOPBP1 TNFRSF6 0.26 39 11 17 5 78.0% 77.3% 2.1E−06 0.0046 50 22 NFKB1 TOPBP1 0.23 38 12 17 5 76.0% 77.3% 0.0165 1.4E−05 50 22 SRC TOPBP1 0.23 39 11 17 5 78.0% 77.3% 0.0176 8.7E−06 50 22 NAB2 TOPBP1 0.23 39 11 17 5 78.0% 77.3% 0.0204 0.0205 50 22 FOS PTEN 0.22 38 11 17 5 77.6% 77.3% 0.0001 0.0003 49 22 NAB2 PTEN 0.22 39 11 17 5 78.0% 77.3% 0.0002 0.0237 50 22 EGR2 NAB1 0.20 42 8 17 5 84.0% 77.3% 0.0039 0.0011 50 22

TABLE 4B Colon Normals Sum Group Size 30.6% 69.4% 100% N = 22 50 72  Gene Mean Mean p-val NAB2 20.42 19.91 0.0001 TOPBP1 18.53 18.03 0.0001 EGR1 19.19 19.85 0.0013 NAB1 17.27 16.92 0.0025 NR4A2 21.49 20.88 0.0041 EGR2 23.57 24.11 0.0089 TGFB1 12.43 12.73 0.0114 FOS 15.10 15.59 0.0122 SERPINE1 20.62 21.10 0.0146 PTEN 14.16 13.81 0.0190 PDGFA 19.05 19.40 0.0628 MAP2K1 16.01 15.81 0.0717 ICAM1 16.80 17.05 0.1086 NFKB1 16.85 16.68 0.2021 CEBPB 14.55 14.73 0.2435 CCND2 16.82 16.47 0.2787 RAF1 14.49 14.34 0.2979 S100A6 14.22 14.01 0.3606 THBS1 17.19 17.43 0.3724 CDKN2D 14.95 14.87 0.3830 SMAD3 18.03 17.91 0.4187 SRC 18.16 18.27 0.4484 TP53 16.30 16.23 0.5315 CREBBP 15.12 15.05 0.5858 PLAU 23.92 24.04 0.6141 ALOX5 15.59 15.68 0.6414 TNFRSF6 16.34 16.40 0.6472 EP300 16.43 16.39 0.7457 NFATC2 16.07 16.04 0.8309 JUN 20.86 20.90 0.8333 EGR3 23.01 22.98 0.8957 FGF2 24.57 24.59 0.9403 MAPK1 14.71 14.71 0.9789

TABLE 5A Colon total used Normal 23 (excludes N = 50 Correct missing) 2-gene models and Entropy #normal #normal #cc #cc Correct Classi- # 1-gene models R-sq Correct FALSE Correct FALSE Classification fication p-val 1 p-val 2 # normals disease AXIN2 TNF 0.62 46 3 19 2 93.9% 90.5% 9.0E−10 2.4E−05 49 21 AXIN2 ITGAL 0.62 40 7 19 2 85.1% 90.5% 8.2E−13 3.2E−05 47 21 AXIN2 MTA1 0.61 43 4 19 2 91.5% 90.5% 7.7E−13 4.2E−05 47 21 AXIN2 CCL5 0.60 43 4 19 2 91.5% 90.5% 1.7E−09 7.0E−05 47 21 AXIN2 HMOX1 0.59 42 5 18 3 89.4% 85.7% 5.4E−10 0.0001 47 21 AXIN2 HOXA10 0.58 44 5 18 3 89.8% 85.7% 4.5E−11 0.0002 49 21 AXIN2 DIABLO 0.56 43 6 18 3 87.8% 85.7% 4.1E−12 0.0004 49 21 AXIN2 HMGA1 0.56 43 6 18 3 87.8% 85.7% 5.1E−12 0.0004 49 21 TNF TNFSF5 0.55 42 5 18 3 89.4% 85.7% 1.9E−08 2.3E−08 47 21 AXIN2 SRF 0.55 39 8 18 3 83.0% 85.7% 1.3E−11 0.0006 47 21 AXIN2 IKBKE 0.55 40 7 18 3 85.1% 85.7% 1.2E−10 0.0006 47 21 AXIN2 IRF1 0.54 39 8 17 4 83.0% 81.0% 1.8E−10 0.0008 47 21 HMOX1 MSH6 0.54 41 5 18 3 89.1% 85.7% 3.3E−06 4.1E−09 46 21 AXIN2 C1QA 0.54 38 9 17 4 80.9% 81.0% 5.1E−07 0.0008 47 21 CCR7 TNF 0.53 48 2 20 3 96.0% 87.0% 8.8E−08 0.0001 50 23 MSH6 TNF 0.53 39 8 17 4 83.0% 81.0% 4.9E−08 7.0E−06 47 21 AXIN2 TGFB1 0.53 44 5 18 3 89.8% 85.7% 2.5E−10 0.0020 49 21 AXIN2 BAX 0.53 46 3 18 3 93.9% 85.7% 2.0E−11 0.0021 49 21 AXIN2 NRAS 0.52 41 8 18 3 83.7% 85.7% 1.0E−10 0.0026 49 21 AXIN2 EGR1 0.52 44 5 18 3 89.8% 85.7% 2.1E−07 0.0030 49 21 C1QA MSH6 0.52 37 9 18 3 80.4% 85.7% 1.1E−05 1.6E−06 46 21 AXIN2 C1QB 0.51 44 5 17 4 89.8% 81.0% 3.3E−06 0.0037 49 21 CCL5 TNFSF5 0.51 40 6 18 3 87.0% 85.7% 1.2E−07 6.6E−08 46 21 CCL5 MSH6 0.51 37 10 18 3 78.7% 85.7% 1.9E−05 8.9E−08 47 21 AXIN2 ST14 0.51 41 8 17 4 83.7% 81.0% 8.3E−11 0.0057 49 21 AXIN2 USP7 0.50 40 7 18 3 85.1% 85.7% 7.7E−11 0.0049 47 21 AXIN2 LARGE 0.50 41 8 18 3 83.7% 85.7% 1.5E−10 0.0065 49 21 AXIN2 IFI16 0.50 41 6 17 4 87.2% 81.0% 1.5E−09 0.0058 47 21 AXIN2 MYC 0.50 41 8 18 3 83.7% 85.7% 2.3E−10 0.0068 49 21 CCL5 CCR7 0.50 38 9 18 3 80.9% 85.7% 0.0003 1.2E−07 47 21 MSH6 NRAS 0.50 41 6 18 3 87.2% 85.7% 4.0E−10 3.1E−05 47 21 AXIN2 MTF1 0.49 38 9 18 3 80.9% 85.7% 3.2E−10 0.0092 47 21 CCR7 HMOX1 0.49 40 7 18 3 85.1% 85.7% 4.2E−08 0.0005 47 21 AXIN2 CTSD 0.49 40 9 18 3 81.6% 85.7% 1.4E−10 0.0134 49 21 AXIN2 IL8 0.49 41 8 18 3 83.7% 85.7% 7.4E−08 0.0135 49 21 IRF1 MSH6 0.49 37 9 17 4 80.4% 81.0% 3.8E−05 2.2E−09 46 21 CCR7 HMGA1 0.49 42 8 20 3 84.0% 87.0% 1.1E−10 0.0014 50 23 AXIN2 G6PD 0.48 41 8 18 3 83.7% 85.7% 3.9E−10 0.0154 49 21 AXIN2 DAD1 0.48 39 10 17 4 79.6% 81.0% 1.4E−10 0.0169 49 21 AXIN2 IGF2BP2 0.48 43 6 18 3 87.8% 85.7% 2.7E−10 0.0176 49 21 AXIN2 IGFBP3 0.48 41 8 18 3 83.7% 85.7% 3.2E−10 0.0193 49 21 AXIN2 CASP9 0.48 39 8 18 3 83.0% 85.7% 2.3E−10 0.0170 47 21 AXIN2 NBEA 0.48 45 4 17 4 91.8% 81.0% 1.8E−05 0.0219 49 21 MSH6 TGFB1 0.48 40 7 18 3 85.1% 85.7% 2.8E−09 7.2E−05 47 21 AXIN2 FOS 0.48 41 7 17 4 85.4% 81.0% 4.5E−09 0.0259 48 21 AXIN2 MYD88 0.48 41 8 18 3 83.7% 85.7% 4.3E−10 0.0240 49 21 AXIN2 CD97 0.48 36 10 18 3 78.3% 85.7% 3.2E−10 0.0189 46 21 CCL5 LTA 0.47 39 8 17 4 83.0% 81.0% 8.3E−09 3.6E−07 47 21 ITGAL MSH6 0.47 38 9 17 4 80.9% 81.0% 7.9E−05 3.7E−10 47 21 AXIN2 TIMP1 0.47 41 8 18 3 83.7% 85.7% 3.2E−09 0.0254 49 21 AXIN2 XK 0.47 39 10 18 3 79.6% 85.7% 2.9E−10 0.0261 49 21 C1QB MSH6 0.47 36 11 17 4 76.6% 81.0% 0.0001 1.9E−05 47 21 IFI16 MSH6 0.47 39 8 17 4 83.0% 81.0% 0.0001 6.0E−09 47 21 AXIN2 ZNF185 0.47 40 7 17 4 85.1% 81.0% 5.0E−10 0.0285 47 21 AXIN2 S100A4 0.47 41 8 18 3 83.7% 85.7% 2.7E−10 0.0363 49 21 AXIN2 PLXDC2 0.47 43 6 17 4 87.8% 81.0% 6.2E−10 0.0377 49 21 CNKSR2 TNF 0.47 44 5 18 3 89.8% 85.7% 9.7E−07 5.0E−05 49 21 AXIN2 GNB1 0.47 42 7 17 4 85.7% 81.0% 3.0E−10 0.0385 49 21 AXIN2 UBE2C 0.47 38 9 17 4 80.9% 81.0% 1.0E−08 0.0312 47 21 AXIN2 VIM 0.47 40 7 17 4 85.1% 81.0% 4.0E−10 0.0323 47 21 AXIN2 LGALS8 0.46 40 7 17 4 85.1% 81.0% 4.8E−10 0.0334 47 21 CCR7 EGR1 0.46 39 11 20 3 78.0% 87.0% 6.3E−06 0.0040 50 23 CCR7 IL8 0.46 42 8 19 4 84.0% 82.6% 1.2E−07 0.0044 50 23 C1QA ZNF350 0.46 38 9 17 4 80.9% 81.0% 4.1E−05 2.0E−05 47 21 C1QB ZNF350 0.46 38 11 17 4 77.6% 81.0% 5.8E−05 4.1E−05 49 21 AXIN2 CCL3 0.46 40 7 18 3 85.1% 85.7% 1.2E−09 0.0493 47 21 AXIN2 NUDT4 0.46 38 9 17 4 80.9% 81.0% 3.0E−09 0.0496 47 21 CCR7 HOXA10 0.46 42 7 17 4 85.7% 81.0% 1.2E−08 0.0027 49 21 C1QB CCR7 0.45 40 9 17 4 81.6% 81.0% 0.0029 5.0E−05 49 21 CCR7 TGFB1 0.45 43 7 20 3 86.0% 87.0% 7.7E−09 0.0068 50 23 CCR7 MYC 0.45 41 9 18 5 82.0% 78.3% 3.9E−10 0.0081 50 23 DIABLO MSH6 0.45 37 10 17 4 78.7% 81.0% 0.0002 8.8E−10 47 21 MSH6 SRF 0.45 39 7 17 4 84.8% 81.0% 1.2E−09 0.0002 46 21 CCR7 IRF1 0.45 39 8 17 4 83.0% 81.0% 1.1E−08 0.0030 47 21 HMOX1 ZNF350 0.45 37 10 18 3 78.7% 85.7% 7.7E−05 2.7E−07 47 21 MSH6 MTF1 0.44 38 9 17 4 80.9% 81.0% 2.5E−09 0.0003 47 21 BAX MSH6 0.44 40 7 17 4 85.1% 81.0% 0.0004 1.2E−09 47 21 CCR7 TIMP1 0.44 42 8 19 4 84.0% 82.6% 1.0E−08 0.0135 50 23 CCR7 NRAS 0.44 39 11 18 5 78.0% 78.3% 3.2E−09 0.0155 50 23 CCR7 ITGAL 0.44 37 10 17 4 78.7% 81.0% 1.9E−09 0.0051 47 21 GSK3B S100A11 0.43 39 8 17 4 83.0% 81.0% 9.1E−09 1.7E−07 47 21 GSK3B TNF 0.43 41 8 18 3 83.7% 85.7% 4.6E−06 1.6E−07 49 21 CNKSR2 HMOX1 0.43 39 8 18 3 83.0% 85.7% 5.4E−07 0.0002 47 21 HMOX1 TNFSF5 0.43 37 10 18 3 78.7% 85.7% 4.1E−06 5.5E−07 47 21 CCL5 CNKSR2 0.43 41 6 18 3 87.2% 85.7% 0.0003 2.7E−06 47 21 CCR7 ZNF350 0.43 43 6 17 4 87.8% 81.0% 0.0002 0.0095 49 21 APC C1QB 0.43 37 12 17 4 75.5% 81.0% 0.0002 3.8E−06 49 21 NRAS ZNF350 0.43 40 9 18 3 81.6% 85.7% 0.0003 7.1E−09 49 21 MSH6 MTA1 0.43 36 11 17 4 76.6% 81.0% 2.2E−09 0.0007 47 21 CCR7 SPARC 0.43 38 9 17 4 80.9% 81.0% 6.8E−06 0.0085 47 21 APC HMOX1 0.42 40 7 17 4 85.1% 81.0% 7.0E−07 3.1E−06 47 21 C1QA MLH1 0.42 39 7 17 4 84.8% 81.0% 2.0E−07 9.2E−05 46 21 HOXA10 MSH6 0.42 40 7 18 3 85.1% 85.7% 0.0009 5.2E−08 47 21 C1QA TNFSF5 0.42 40 7 18 3 85.1% 85.7% 6.0E−06 0.0001 47 21 CCR7 SRF 0.42 40 7 17 4 85.1% 81.0% 3.7E−09 0.0110 47 21 APC C1QA 0.42 38 9 17 4 80.9% 81.0% 0.0001 3.8E−06 47 21 CCR7 MYD88 0.42 39 11 18 5 78.0% 78.3% 2.1E−09 0.0397 50 23 CCR7 G6PD 0.42 39 11 18 5 78.0% 78.3% 5.4E−09 0.0397 50 23 MSH6 S100A4 0.42 41 6 18 3 87.2% 85.7% 3.1E−09 0.0010 47 21 TNF ZNF350 0.42 38 11 16 5 77.6% 76.2% 0.0004 8.4E−06 49 21 CCR7 SERPINE1 0.42 43 7 20 3 86.0% 87.0% 7.1E−09 0.0419 50 23 IFI16 ZNF350 0.42 40 7 18 3 85.1% 85.7% 0.0003 5.8E−08 47 21 AXIN2 0.42 41 8 17 4 83.7% 81.0% 2.4E−09 49 21 CASP9 MSH6 0.42 39 8 17 4 83.0% 81.0% 0.0011 3.5E−09 47 21 MSH6 TIMP1 0.41 37 10 16 5 78.7% 76.2% 5.7E−08 0.0013 47 21 APC TNFRSF1A 0.41 40 9 17 4 81.6% 81.0% 6.1E−09 7.0E−06 49 21 GSK3B PLXDC2 0.41 40 9 17 4 81.6% 81.0% 6.9E−09 3.8E−07 49 21 C1QB GSK3B 0.41 38 11 17 4 77.6% 81.0% 3.8E−07 0.0003 49 21 MLH1 TNF 0.41 37 10 17 4 78.7% 81.0% 8.7E−06 3.9E−07 47 21 CCR7 IFI16 0.41 38 9 17 4 80.9% 81.0% 8.0E−08 0.0181 47 21 CCR7 DIABLO 0.41 37 12 16 5 75.5% 76.2% 3.9E−09 0.0265 49 21 CCR7 USP7 0.41 38 9 16 5 80.9% 76.2% 5.5E−09 0.0219 47 21 IRF1 ZNF350 0.41 39 8 18 3 83.0% 85.7% 0.0005 7.1E−08 47 21 HMOX1 MLH1 0.40 39 7 18 3 84.8% 85.7% 4.4E−07 1.6E−06 46 21 MSH6 MYD88 0.40 37 10 16 5 78.7% 76.2% 1.3E−08 0.0019 47 21 APC IRF1 0.40 39 8 17 4 83.0% 81.0% 7.6E−08 7.6E−06 47 21 CCR7 E2F1 0.40 41 6 17 4 87.2% 81.0% 3.5E−06 0.0248 47 21 TNFRSF1A ZNF350 0.40 40 9 17 4 81.6% 81.0% 0.0008 9.6E−09 49 21 G6PD MSH6 0.40 38 9 17 4 80.9% 81.0% 0.0021 2.1E−08 47 21 C1QA TXNRD1 0.40 37 10 18 3 78.7% 85.7% 2.1E−07 0.0003 47 21 MAPK14 MSH6 0.40 36 11 16 5 76.6% 76.2% 0.0024 8.3E−09 47 21 C1QA GSK3B 0.40 39 8 17 4 83.0% 81.0% 5.8E−07 0.0003 47 21 TNF XRCC1 0.40 43 6 17 4 87.8% 81.0% 2.2E−08 2.0E−05 49 21 MSH6 USP7 0.40 37 9 17 4 80.4% 81.0% 9.1E−09 0.0021 46 21 NBEA TNF 0.40 40 9 17 4 81.6% 81.0% 2.2E−05 0.0007 49 21 MSH2 TNF 0.40 42 8 20 3 84.0% 87.0% 6.1E−05 0.0012 50 23 CCR7 ING2 0.40 37 12 17 4 75.5% 81.0% 3.5E−06 0.0487 49 21 C1QB TXNRD1 0.39 38 9 17 4 80.9% 81.0% 2.7E−07 0.0007 47 21 HMOX1 MSH2 0.39 41 6 18 3 87.2% 85.7% 0.0002 2.6E−06 47 21 MSH6 UBE2C 0.39 37 9 16 5 80.4% 76.2% 2.2E−07 0.0024 46 21 APC TNF 0.39 39 10 17 4 79.6% 81.0% 2.5E−05 1.6E−05 49 21 CCR7 MTF1 0.39 37 10 16 5 78.7% 76.2% 2.3E−08 0.0404 47 21 DAD1 MSH6 0.39 39 8 17 4 83.0% 81.0% 0.0033 1.1E−08 47 21 GSK3B HMOX1 0.39 38 9 17 4 80.9% 81.0% 2.9E−06 8.1E−07 47 21 MYD88 ZNF350 0.39 39 10 16 5 79.6% 76.2% 0.0013 1.9E−08 49 21 LTA TNF 0.39 37 10 16 5 78.7% 76.2% 2.2E−05 3.4E−07 47 21 C1QA MSH2 0.39 37 10 17 4 78.7% 81.0% 0.0003 0.0005 47 21 MSH6 PLXDC2 0.39 36 11 17 4 76.6% 81.0% 2.5E−08 0.0038 47 21 CTSD MSH6 0.39 37 10 17 4 78.7% 81.0% 0.0039 1.4E−08 47 21 APC S100A11 0.39 37 10 16 5 78.7% 76.2% 6.9E−08 2.0E−05 47 21 CD59 ZNF350 0.39 42 7 18 3 85.7% 85.7% 0.0015 3.1E−08 49 21 C1QB TNFSF5 0.39 40 7 17 4 85.1% 81.0% 2.8E−05 0.0010 47 21 C1QA CNKSR2 0.38 39 8 18 3 83.0% 85.7% 0.0016 0.0006 47 21 C1QB NBEA 0.38 39 10 17 4 79.6% 81.0% 0.0013 0.0012 49 21 C1QB MLH1 0.38 37 10 17 4 78.7% 81.0% 1.2E−06 0.0008 47 21 MSH6 RBM5 0.38 38 9 17 4 80.9% 81.0% 4.8E−08 0.0050 47 21 MAPK14 ZNF350 0.38 36 11 17 4 76.6% 81.0% 0.0015 1.7E−08 47 21 TLR2 ZNF350 0.38 41 6 17 4 87.2% 81.0% 0.0014 2.1E−08 47 21 MSH6 TLR2 0.38 37 9 16 5 80.4% 76.2% 2.5E−08 0.0045 46 21 FOS MSH6 0.38 35 11 16 5 76.1% 76.2% 0.0049 4.5E−07 46 21 MSH6 TNFRSF1A 0.38 37 10 16 5 78.7% 76.2% 3.5E−08 0.0058 47 21 MSH2 TGFB1 0.38 43 7 19 4 86.0% 82.6% 2.4E−07 0.0027 50 23 APC IFI16 0.38 37 10 16 5 78.7% 76.2% 3.0E−07 2.8E−05 47 21 MSH6 S100A11 0.38 38 9 17 4 80.9% 81.0% 9.9E−08 0.0061 47 21 C1QB CNKSR2 0.38 37 12 18 3 75.5% 85.7% 0.0028 0.0016 49 21 CCL5 XRCC1 0.38 38 9 17 4 80.9% 81.0% 7.1E−08 2.6E−05 47 21 APC MAPK14 0.38 39 8 16 5 83.0% 76.2% 2.2E−08 3.1E−05 47 21 APC PLXDC2 0.38 38 11 16 5 77.6% 76.2% 3.2E−08 3.4E−05 49 21 CA4 MSH6 0.38 36 10 16 5 78.3% 76.2% 0.0054 3.4E−07 46 21 CNKSR2 ZNF350 0.38 38 11 17 4 77.6% 81.0% 0.0025 0.0032 49 21 CNKSR2 HMGA1 0.38 42 7 18 3 85.7% 85.7% 1.9E−08 0.0032 49 21 C1QB ING2 0.37 37 12 16 5 75.5% 76.2% 9.3E−06 0.0019 49 21 HMOX1 IKBKE 0.37 39 8 17 4 83.0% 81.0% 2.6E−07 6.5E−06 47 21 CA4 ZNF350 0.37 36 11 17 4 76.6% 81.0% 0.0021 3.2E−07 47 21 HMOX1 TXNRD1 0.37 37 10 17 4 78.7% 81.0% 7.2E−07 6.6E−06 47 21 CCR7 0.37 39 11 18 5 78.0% 78.3% 5.9E−09 50 23 CCL5 MLH1 0.37 39 8 17 4 83.0% 81.0% 2.1E−06 3.3E−05 47 21 G6PD GSK3B 0.37 39 10 16 5 79.6% 76.2% 2.4E−06 5.9E−08 49 21 MSH6 NBEA 0.37 36 11 17 4 76.6% 81.0% 0.0020 0.0091 47 21 C1QB MSH2 0.37 38 11 17 4 77.6% 81.0% 0.0009 0.0023 49 21 MSH6 SPARC 0.37 35 11 16 5 76.1% 76.2% 6.8E−05 0.0075 46 21 TGFB1 ZNF350 0.37 40 9 17 4 81.6% 81.0% 0.0034 2.7E−07 49 21 C1QA NBEA 0.37 37 10 16 5 78.7% 76.2% 0.0024 0.0012 47 21 CNKSR2 IL8 0.37 40 9 17 4 81.6% 81.0% 1.7E−05 0.0053 49 21 CNKSR2 NRAS 0.36 40 9 18 3 81.6% 85.7% 1.1E−07 0.0055 49 21 APC TGFB1 0.36 42 7 17 4 85.7% 81.0% 3.4E−07 6.1E−05 49 21 MSH6 ST14 0.36 38 9 17 4 80.9% 81.0% 5.5E−08 0.0131 47 21 GSK3B TIMP1 0.36 39 10 17 4 79.6% 81.0% 4.6E−07 3.5E−06 49 21 EGR1 TNFSF5 0.36 38 9 16 5 80.9% 76.2% 8.0E−05 0.0002 47 21 CD97 MSH6 0.36 37 9 16 5 80.4% 76.2% 0.0108 4.0E−08 46 21 MTF1 ZNF350 0.36 36 11 16 5 76.6% 76.2% 0.0040 8.8E−08 47 21 FOS ZNF350 0.36 39 9 17 4 81.3% 81.0% 0.0040 6.7E−07 48 21 ADAM17 C1QA 0.36 38 8 17 4 82.6% 81.0% 0.0014 1.6E−06 46 21 TNF TXNRD1 0.36 36 11 17 4 76.6% 81.0% 1.2E−06 1.0E−04 47 21 MSH6 VIM 0.36 36 10 16 5 78.3% 76.2% 4.2E−08 0.0112 46 21 CNKSR2 SPARC 0.36 41 6 17 4 87.2% 81.0% 0.0001 0.0048 47 21 E2F1 MSH6 0.36 36 10 16 5 78.3% 76.2% 0.0116 1.9E−05 46 21 APC MYD88 0.36 40 9 17 4 81.6% 81.0% 6.9E−08 7.2E−05 49 21 HMOX1 XRCC1 0.36 37 10 16 5 78.7% 76.2% 1.4E−07 1.2E−05 47 21 PLXDC2 ZNF350 0.36 39 10 16 5 79.6% 76.2% 0.0054 6.9E−08 49 21 NBEA SPARC 0.36 39 8 18 3 83.0% 85.7% 0.0001 0.0038 47 21 CNKSR2 EGR1 0.36 43 6 18 3 87.8% 85.7% 0.0003 0.0075 49 21 HMGA1 MSH6 0.36 37 10 17 4 78.7% 81.0% 0.0180 5.5E−08 47 21 CNKSR2 NBEA 0.35 38 11 16 5 77.6% 76.2% 0.0054 0.0091 49 21 EGR1 ZNF350 0.35 39 10 17 4 79.6% 81.0% 0.0074 0.0004 49 21 APC G6PD 0.35 39 10 16 5 79.6% 76.2% 1.3E−07 0.0001004 49 21 CNKSR2 IRF1 0.35 39 8 17 4 83.0% 81.0% 6.9E−07 0.0069 47 21 MSH6 XK 0.35 37 10 17 4 78.7% 81.0% 8.5E−08 0.0228 47 21 C1QB LTA 0.35 39 8 17 4 83.0% 81.0% 1.9E−06 0.0040 47 21 MSH6 SERPINE1 0.35 36 11 16 5 76.6% 76.2% 3.9E−07 0.0239 47 21 MSH2 NRAS 0.35 39 11 19 4 78.0% 82.6% 1.9E−07 0.0122 50 23 APC CA4 0.35 37 10 16 5 78.7% 76.2% 8.8E−07 8.3E−05 47 21 BAX MSH2 0.35 38 12 19 4 76.0% 82.6% 0.0125 2.3E−08 50 23 HOXA10 ZNF350 0.35 39 10 16 5 79.6% 76.2% 0.0090 1.3E−06 49 21 EGR1 NBEA 0.35 41 8 16 5 83.7% 76.2% 0.0067 0.0004 49 21 BCAM MSH6 0.35 38 8 17 4 82.6% 81.0% 0.0205 9.9E−08 46 21 CAV1 MSH6 0.35 37 10 17 4 78.7% 81.0% 0.0266 2.4E−06 47 21 SIAH2 XK 0.35 37 10 17 4 78.7% 81.0% 1.1E−07 2.7E−05 47 21 APC TLR2 0.35 40 7 18 3 85.1% 85.7% 9.7E−08 9.8E−05 47 21 CCL5 ZNF350 0.35 37 10 16 5 78.7% 76.2% 0.0086 0.0001 47 21 APC FOS 0.35 38 10 17 4 79.2% 81.0% 1.4E−06 0.0001 48 21 MSH6 PLAU 0.34 36 11 16 5 76.6% 76.2% 7.7E−08 0.0313 47 21 MSH6 RP51077B9.4 0.34 36 11 16 5 76.6% 76.2% 1.8E−06 0.0318 47 21 NBEA ZNF350 0.34 39 10 16 5 79.6% 76.2% 0.0115 0.0085 49 21 ADAM17 HMOX1 0.34 36 10 16 5 78.3% 76.2% 2.3E−05 3.6E−06 46 21 CNKSR2 E2F1 0.34 37 10 17 4 78.7% 81.0% 4.8E−05 0.0107 47 21 GSK3B TGFB1 0.34 40 9 16 5 81.6% 76.2% 8.8E−07 8.5E−06 49 21 CNKSR2 HOXA10 0.34 43 6 17 4 87.8% 81.0% 1.7E−06 0.0160 49 21 MSH2 S100A4 0.34 41 9 19 4 82.0% 82.6% 3.4E−08 0.0191 50 23 ETS2 MSH6 0.34 36 11 16 5 76.6% 76.2% 0.0380 1.1E−07 47 21 MNDA MSH6 0.34 38 9 17 4 80.9% 81.0% 0.0389 1.0E−07 47 21 MSH6 SERPINA1 0.34 37 10 16 5 78.7% 76.2% 9.1E−08 0.0389 47 21 C1QB CEACAM1 0.34 39 10 17 4 79.6% 81.0% 1.4E−07 0.0094 49 21 CNKSR2 TGFB1 0.34 41 8 18 3 83.7% 85.7% 9.9E−07 0.0175 49 21 CNKSR2 MSH6 0.34 38 9 17 4 80.9% 81.0% 0.0405 0.0153 47 21 APC MTF1 0.34 36 11 16 5 76.6% 76.2% 2.4E−07 0.0002 47 21 C1QA IKBKE 0.34 36 11 16 5 76.6% 76.2% 1.1E−06 0.0045 47 21 G6PD ZNF350 0.34 39 10 16 5 79.6% 76.2% 0.0147 2.4E−07 49 21 HOXA10 TNFSF5 0.34 38 9 17 4 80.9% 81.0% 0.0002 2.2E−06 47 21 PTPRK TNF 0.34 39 11 19 4 78.0% 82.6% 0.0010 2.0E−06 50 23 IQGAP1 TNF 0.34 39 11 18 5 78.0% 78.3% 0.0010 8.3E−08 50 23 MSH2 NBEA 0.34 38 11 16 5 77.6% 76.2% 0.0113 0.0038 49 21 IRF1 MSH2 0.34 36 11 16 5 76.6% 76.2% 0.0032 1.3E−06 47 21 CCL5 IKBKE 0.34 36 10 17 4 78.3% 81.0% 1.9E−06 0.0001 46 21 CNKSR2 IFI16 0.34 39 8 17 4 83.0% 81.0% 1.9E−06 0.0168 47 21 CCL3 MSH6 0.34 35 11 16 5 76.1% 76.2% 0.0349 2.1E−07 46 21 IL8 MSH6 0.34 39 8 17 4 83.0% 81.0% 0.0457 4.9E−05 47 21 GSK3B MAPK14 0.34 37 10 17 4 78.7% 81.0% 1.3E−07 1.2E−05 47 21 MMP9 MSH6 0.34 37 10 17 4 78.7% 81.0% 0.0469 2.7E−07 47 21 CNKSR2 MSH2 0.34 38 11 17 4 77.6% 81.0% 0.0041 0.0211 49 21 CA4 MME 0.34 38 9 16 5 80.9% 76.2% 1.2E−06 1.6E−06 47 21 EGR1 MSH2 0.34 39 11 19 4 78.0% 82.6% 0.0251 0.0031 50 23 IKBKE TNF 0.33 41 6 16 5 87.2% 76.2% 0.0003 1.4E−06 47 21 NBEA SIAH2 0.33 37 10 16 5 78.7% 76.2% 4.7E−05 0.0116 47 21 CNKSR2 MYC 0.33 44 5 17 4 89.8% 81.0% 4.0E−07 0.0251 49 21 SRF ZNF350 0.33 37 10 16 5 78.7% 76.2% 0.0138 1.7E−07 47 21 SPARC TNFSF5 0.33 38 9 17 4 80.9% 81.0% 0.0003 0.0005 47 21 GSK3B TNFRSF1A 0.33 39 10 17 4 79.6% 81.0% 2.3E−07 1.5E−05 49 21 CCL5 NBEA 0.33 36 11 17 4 76.6% 81.0% 0.0134 0.0002 47 21 CAV1 ZNF350 0.33 39 10 17 4 79.6% 81.0% 0.0228 4.6E−06 49 21 CNKSR2 MTA1 0.33 39 8 17 4 83.0% 81.0% 1.4E−07 0.0244 47 21 LGALS8 ZNF350 0.33 37 10 16 5 78.7% 76.2% 0.0181 1.7E−07 47 21 APC NRAS 0.33 42 7 16 5 85.7% 76.2% 5.1E−07 0.0003 49 21 ADAM17 TNF 0.33 36 11 16 5 76.6% 76.2% 0.0003 7.3E−06 47 21 GNB1 TNF 0.33 43 6 17 4 87.8% 81.0% 0.0005 1.3E−07 49 21 MNDA ZNF350 0.33 36 11 16 5 76.6% 76.2% 0.0193 1.7E−07 47 21 ETS2 ZNF350 0.33 37 12 16 5 75.5% 76.2% 0.0255 1.5E−07 49 21 CTSD ZNF350 0.33 38 11 16 5 77.6% 76.2% 0.0255 1.6E−07 49 21 APC CNKSR2 0.33 38 11 17 4 77.6% 81.0% 0.0323 0.0003 49 21 ETS2 GSK3B 0.33 38 11 16 5 77.6% 76.2% 1.7E−05 1.5E−07 49 21 SPARC ZNF350 0.33 38 9 16 5 80.9% 76.2% 0.0169 0.0005 47 21 CNKSR2 SERPING1 0.33 39 10 17 4 79.6% 81.0% 1.0E−06 0.0331 49 21 G6PD MSH2 0.33 43 7 18 5 86.0% 78.3% 0.0405 4.1E−07 50 23 C1QB IL8 0.33 41 8 18 3 83.7% 85.7% 9.7E−05 0.0182 49 21 C1QA LGALS8 0.33 38 8 17 4 82.6% 81.0% 2.1E−07 0.0072 46 21 CNKSR2 ITGAL 0.33 38 9 17 4 80.9% 81.0% 2.4E−07 0.0299 47 21 FOS MSH2 0.32 40 9 19 4 81.6% 82.6% 0.0436 2.1E−06 49 23 UBE2C ZNF350 0.32 38 9 17 4 80.9% 81.0% 0.0192 4.7E−06 47 21 IL8 MSH2 0.32 39 11 18 5 78.0% 78.3% 0.0448 8.0E−05 50 23 HMOX1 RBM5 0.32 39 7 17 4 84.8% 81.0% 6.0E−07 5.3E−05 46 21 CNKSR2 ING2 0.32 39 10 17 4 79.6% 81.0% 8.5E−05 0.0381 49 21 APC EGR1 0.32 41 8 17 4 83.7% 81.0% 0.0013 0.0004 49 21 APC SERPINA1 0.32 36 11 16 5 76.6% 76.2% 2.1E−07 0.0004 47 21 E2F1 ZNF350 0.32 36 11 16 5 76.6% 76.2% 0.0229 0.0001 47 21 C1QB PTEN 0.32 38 11 16 5 77.6% 76.2% 8.6E−07 0.0234 49 21 CNKSR2 DIABLO 0.32 38 11 17 4 77.6% 81.0% 1.8E−07 0.0457 49 21 ST14 ZNF350 0.32 37 12 16 5 75.5% 76.2% 0.0359 2.8E−07 49 21 IFI16 TXNRD1 0.32 39 7 16 5 84.8% 76.2% 7.3E−06 4.9E−06 46 21 CAV1 CNKSR2 0.32 39 10 16 5 79.6% 76.2% 0.0480 7.3E−06 49 21 CTNNA1 ZNF350 0.32 37 12 16 5 75.5% 76.2% 0.0378 2.3E−07 49 21 CCL5 PTPRK 0.32 39 8 17 4 83.0% 81.0% 6.8E−06 0.0003 47 21 SERPING1 ZNF350 0.32 39 10 16 5 79.6% 76.2% 0.0391 1.4E−06 49 21 IL8 NBEA 0.32 41 8 16 5 83.7% 76.2% 0.0297 0.0001 49 21 C1QB MME 0.32 39 8 16 5 83.0% 76.2% 2.7E−06 0.0232 47 21 CCL5 MYC 0.32 36 11 16 5 76.6% 76.2% 8.6E−07 0.0004 47 21 GSK3B IRF1 0.32 36 11 16 5 76.6% 76.2% 3.3E−06 2.1E−05 47 21 CNKSR2 USP7 0.32 40 7 18 3 85.1% 85.7% 2.6E−07 0.0380 47 21 EGR1 GSK3B 0.32 39 10 16 5 79.6% 76.2% 2.7E−05 0.0019 49 21 IL8 ZNF350 0.32 40 9 17 4 81.6% 81.0% 0.0444 0.0002 49 21 BAX ZNF350 0.32 38 11 16 5 77.6% 76.2% 0.0450 2.0E−07 49 21 C1QB XRCC1 0.32 39 10 16 5 79.6% 76.2% 8.5E−07 0.0308 49 21 NBEA SERPINE1 0.32 40 9 17 4 81.6% 81.0% 1.3E−06 0.0340 49 21 RBM5 TNF 0.32 36 11 16 5 76.6% 76.2% 0.0006 9.1E−07 47 21 C1QA RBM5 0.32 36 10 16 5 78.3% 76.2% 8.9E−07 0.0119 46 21 MSH2 ZNF350 0.31 42 7 16 5 85.7% 76.2% 0.0485 0.0113 49 21 TGFB1 TNFSF5 0.31 38 9 17 4 80.9% 81.0% 0.0007 4.2E−06 47 21 C1QA SIAH2 0.31 36 10 16 5 78.3% 76.2% 0.0001 0.0133 46 21 C1QB IQGAP1 0.31 38 11 16 5 77.6% 76.2% 5.3E−07 0.0368 49 21 CNKSR2 SRF 0.31 36 11 17 4 76.6% 81.0% 4.0E−07 0.0487 47 21 HMOX1 ING2 0.31 36 11 16 5 76.6% 76.2% 0.0001 0.0001004 47 21 EGR1 PTPRK 0.31 40 10 19 4 80.0% 82.6% 7.3E−06 0.0106 50 23 C1QA MME 0.31 38 9 17 4 80.9% 81.0% 3.7E−06 0.0170 47 21 C1QA ESR1 0.31 40 7 17 4 85.1% 81.0% 3.0E−06 0.0171 47 21 C1QB CCL5 0.31 38 9 17 4 80.9% 81.0% 0.0005 0.0257 47 21 C1QB TNF 0.31 39 10 17 4 79.6% 81.0% 0.0011 0.0393 49 21 HOXA10 NBEA 0.31 38 11 16 5 77.6% 76.2% 0.0429 7.0E−06 49 21 C1QB SPARC 0.31 38 9 17 4 80.9% 81.0% 0.0011 0.0329 47 21 ITGAL TNFSF5 0.31 39 7 16 5 84.8% 76.2% 0.0009 5.2E−07 46 21 NRAS TNFSF5 0.31 39 8 17 4 83.0% 81.0% 0.0008 1.4E−06 47 21 E2F1 NBEA 0.31 39 8 16 5 83.0% 76.2% 0.0365 0.0002 47 21 ADAM17 MTF1 0.31 37 10 17 4 78.7% 81.0% 8.5E−07 1.7E−05 47 21 NBEA TIMP1 0.31 37 12 16 5 75.5% 76.2% 4.9E−06 0.0467 49 21 C1QA CD97 0.31 38 8 17 4 82.6% 81.0% 3.9E−07 0.0161 46 21 C1QB SP1 0.31 37 10 17 4 78.7% 81.0% 3.9E−07 0.0364 47 21 E2F1 TNFSF5 0.31 36 11 17 4 76.6% 81.0% 0.0009 0.0002 47 21 C1QA SPARC 0.31 39 8 17 4 83.0% 81.0% 0.0013 0.0194 47 21 C1QB RBM5 0.31 37 10 16 5 78.7% 76.2% 1.3E−06 0.0309 47 21 CCL5 MTA1 0.31 37 10 17 4 78.7% 81.0% 4.0E−07 0.0006 47 21 TNFSF5 USP7 0.31 37 10 16 5 78.7% 76.2% 4.2E−07 0.0010 47 21 C1QA IL8 0.31 40 7 17 4 85.1% 81.0% 0.0002 0.0222 47 21 CCL5 MSH2 0.30 38 9 17 4 80.9% 81.0% 0.0139 0.0007 47 21 SPARC TXNRD1 0.30 40 7 17 4 85.1% 81.0% 1.4E−05 0.0015 47 21 CTSD GSK3B 0.30 38 11 16 5 77.6% 76.2% 4.8E−05 4.5E−07 49 21 CA4 MSH2 0.30 38 9 16 5 80.9% 76.2% 0.0157 6.5E−06 47 21 C1QB PTPRC 0.30 39 8 16 5 83.0% 76.2% 6.2E−07 0.0369 47 21 MSH2 XK 0.30 38 11 16 5 77.6% 76.2% 5.4E−07 0.0197 49 21 APC TEGT 0.30 39 10 16 5 79.6% 76.2% 3.6E−07 0.0010 49 21 IRF1 TXNRD1 0.30 39 8 17 4 83.0% 81.0% 1.5E−05 6.1E−06 47 21 EGR1 TXNRD1 0.30 37 10 17 4 78.7% 81.0% 1.6E−05 0.0036 47 21 APC CTSD 0.30 39 10 17 4 79.6% 81.0% 5.3E−07 0.0011 49 21 IGF2BP2 SIAH2 0.30 40 7 18 3 85.1% 85.7% 0.0002 9.7E−07 47 21 C1QA MYC 0.30 38 9 17 4 80.9% 81.0% 1.8E−06 0.0289 47 21 HMOX1 LTA 0.30 37 9 17 4 80.4% 81.0% 1.5E−05 0.0002 46 21 C1QA TNF 0.30 36 11 16 5 76.6% 76.2% 0.0017 0.0322 47 21 IFI16 MSH2 0.30 36 11 17 4 76.6% 81.0% 0.0194 1.1E−05 47 21 ING2 SPARC 0.29 38 9 16 5 80.9% 76.2% 0.0024 0.0003 47 21 C1QA PTPRK 0.29 39 8 17 4 83.0% 81.0% 2.1E−05 0.0412 47 21 APC ETS2 0.29 37 12 16 5 75.5% 76.2% 7.4E−07 0.0016 49 21 GSK3B SERPINA1 0.29 37 10 17 4 78.7% 81.0% 7.5E−07 8.6E−05 47 21 C1QA CCL5 0.29 35 11 17 4 76.1% 81.0% 0.0010 0.0364 46 21 C1QA GNB1 0.29 40 7 16 5 85.1% 76.2% 8.1E−07 0.0440 47 21 NCOA1 TNF 0.29 38 12 18 5 76.0% 78.3% 0.0104 2.9E−07 50 23 IL8 TNF 0.29 39 11 18 5 78.0% 78.3% 0.0105 0.0004 50 23 G6PD TXNRD1 0.29 42 5 16 5 89.4% 76.2% 2.6E−05 3.2E−06 47 21 C1QA IQGAP1 0.29 37 10 16 5 78.7% 76.2% 1.5E−06 0.0459 47 21 GNB1 HMOX1 0.29 38 9 16 5 80.9% 76.2% 0.0003 8.8E−07 47 21 MSH6 0.29 37 10 17 4 78.7% 81.0% 8.1E−07 47 21 MTA1 TNFSF5 0.29 36 10 17 4 78.3% 81.0% 0.0024 9.7E−07 46 21 EGR1 MYC 0.29 38 12 18 5 76.0% 78.3% 7.6E−07 0.0363 50 23 GSK3B NRAS 0.29 38 11 17 4 77.6% 81.0% 3.4E−06 0.0001 49 21 TIMP1 TNFSF5 0.29 42 5 16 5 89.4% 76.2% 0.0025 2.1E−05 47 21 MSH2 SPARC 0.28 38 9 17 4 80.9% 81.0% 0.0041 0.0440 47 21 MSH2 0.28 41 9 19 4 82.0% 82.6% 4.4E−07 50 23 IQGAP1 TIMP1 0.28 38 12 18 5 76.0% 78.3% 1.9E−05 1.3E−06 50 23 APC CTNNA1 0.28 37 12 16 5 75.5% 76.2% 1.3E−06 0.0029 49 21 ADAM17 S100A11 0.28 39 8 16 5 83.0% 76.2% 7.7E−06 6.6E−05 47 21 HMOX1 MYC 0.28 38 9 18 3 80.9% 85.7% 5.3E−06 0.0005 47 21 LTA SPARC 0.28 36 10 16 5 78.3% 76.2% 0.0047 4.5E−05 46 21 CNKSR2 0.27 39 10 17 4 79.6% 81.0% 1.3E−06 49 21 ADAM17 IRF1 0.27 37 9 17 4 80.4% 81.0% 2.3E−05 7.7E−05 46 21 LARGE TNF 0.27 39 10 16 5 79.6% 76.2% 0.0064 3.7E−06 49 21 SIAH2 TNF 0.27 36 11 16 5 76.6% 76.2% 0.0044 0.0007 47 21 CCL5 ING2 0.27 37 10 16 5 78.7% 76.2% 0.0012 0.0031 47 21 EGR1 MLH1 0.27 37 10 16 5 78.7% 76.2% 0.0002 0.0133 47 21 CCL5 GNB1 0.27 36 11 16 5 76.6% 76.2% 2.2E−06 0.0034 47 21 HMOX1 SIAH2 0.27 36 10 16 5 78.3% 76.2% 0.0007 0.0006 46 21 HMOX1 LGALS8 0.27 38 8 16 5 82.6% 76.2% 2.6E−06 0.0006 46 21 E2F1 ING2 0.27 36 11 16 5 76.6% 76.2% 0.0010 0.0014 47 21 SRF TNFSF5 0.26 37 10 16 5 78.7% 76.2% 0.0066 3.1E−06 47 21 EGR1 SIAH2 0.26 37 10 16 5 78.7% 76.2% 0.0010 0.0184 47 21 MLH1 TGFB1 0.26 36 11 16 5 76.6% 76.2% 3.2E−05 0.0003 47 21 DIABLO TNFSF5 0.26 36 11 16 5 76.6% 76.2% 0.0078 3.0E−06 47 21 HMOX1 MME 0.26 38 9 17 4 80.9% 81.0% 3.3E−05 0.0009 47 21 ING2 NRAS 0.26 40 9 16 5 81.6% 76.2% 1.1E−05 0.0015 49 21 C1QB 0.26 39 10 17 4 79.6% 81.0% 2.3E−06 49 21 CCL5 SIAH2 0.26 36 11 16 5 76.6% 76.2% 0.0012 0.0051 47 21 ING2 S100A11 0.26 36 11 16 5 76.6% 76.2% 1.8E−05 0.0019 47 21 CCL5 LARGE 0.26 37 10 16 5 78.7% 76.2% 9.8E−06 0.0060 47 21 APC MNDA 0.26 36 11 16 5 76.6% 76.2% 3.8E−06 0.0070 47 21 GSK3B TLR2 0.26 37 10 17 4 78.7% 81.0% 4.7E−06 0.0003 47 21 IL8 ING2 0.26 37 12 16 5 75.5% 76.2% 0.0019 0.0024 49 21 SPARC XRCC1 0.26 37 10 16 5 78.7% 76.2% 1.3E−05 0.0140 47 21 DIABLO HMOX1 0.26 38 9 17 4 80.9% 81.0% 0.0012 3.7E−06 47 21 CCL5 DIABLO 0.26 37 10 16 5 78.7% 76.2% 3.8E−06 0.0062 47 21 MLH1 SPARC 0.26 36 10 16 5 78.3% 76.2% 0.0115 0.0003 46 21 ADAM17 MAPK14 0.26 36 11 16 5 76.6% 76.2% 4.3E−06 0.0002 47 21 APC HSPA1A 0.25 37 12 16 5 75.5% 76.2% 3.3E−06 0.0101 49 21 PTPRK SPARC 0.25 37 10 17 4 78.7% 81.0% 0.0163 0.0001 47 21 EGR1 GNB1 0.25 38 11 16 5 77.6% 76.2% 3.7E−06 0.0398 49 21 IQGAP1 MYD88 0.25 39 11 18 5 78.0% 78.3% 5.5E−06 4.9E−06 50 23 TNF USP7 0.25 37 10 17 4 78.7% 81.0% 4.6E−06 0.0155 47 21 G6PD TNFSF5 0.25 40 7 16 5 85.1% 76.2% 0.0133 1.9E−05 47 21 CCL5 EGR1 0.25 38 9 16 5 80.9% 76.2% 0.0362 0.0083 47 21 PLEK2 SIAH2 0.25 37 10 17 4 78.7% 81.0% 0.0020 7.0E−06 47 21 SPARC TNF 0.25 38 9 16 5 80.9% 76.2% 0.0167 0.0196 47 21 ADAM17 TLR2 0.25 35 11 16 5 76.1% 76.2% 7.3E−06 0.0002 46 21 DAD1 TNF 0.25 37 12 16 5 75.5% 76.2% 0.0211 4.3E−06 49 21 EGR1 SPARC 0.25 39 8 16 5 83.0% 76.2% 0.0211 0.0462 47 21 APC BAX 0.25 37 12 16 5 75.5% 76.2% 4.4E−06 0.0138 49 21 EGR1 HMOX1 0.25 36 11 16 5 76.6% 76.2% 0.0018 0.0490 47 21 APC NCOA1 0.25 37 12 16 5 75.5% 76.2% 5.3E−06 0.0142 49 21 ADAM17 TIMP1 0.25 36 11 16 5 76.6% 76.2% 8.6E−05 0.0003 47 21 HMOX1 SPARC 0.24 40 7 17 4 85.1% 81.0% 0.0246 0.0020 47 21 CAV1 TNF 0.24 39 10 16 5 79.6% 76.2% 0.0251 0.0002 49 21 E2F1 TNF 0.24 38 9 16 5 80.9% 76.2% 0.0218 0.0043 47 21 ING2 TNFRSF1A 0.24 39 10 16 5 79.6% 76.2% 1.1E−05 0.0034 49 21 APC SERPINE1 0.24 38 11 16 5 77.6% 76.2% 3.4E−05 0.0163 49 21 C1QA 0.24 37 10 16 5 78.7% 76.2% 6.1E−06 47 21 FOS PTEN 0.24 38 11 18 5 77.6% 78.3% 8.0E−05 0.0001 49 23 SPARC ZNF185 0.24 38 9 17 4 80.9% 81.0% 9.1E−06 0.0280 47 21 HMOX1 PTPRK 0.24 38 9 16 5 80.9% 76.2% 0.0002 0.0023 47 21 CCL5 ITGAL 0.24 36 11 16 5 76.6% 76.2% 9.3E−06 0.0124 47 21 APC CAV1 0.24 38 11 16 5 77.6% 76.2% 0.0002 0.0181 49 21 SIAH2 TNFSF5 0.24 36 10 16 5 78.3% 76.2% 0.0219 0.0027 46 21 MLH1 MTF1 0.24 37 10 16 5 78.7% 76.2% 1.8E−05 0.0007 47 21 EGR1 0.24 39 11 18 5 78.0% 78.3% 3.0E−06 50 23 FOS IL8 0.24 38 11 18 5 77.6% 78.3% 0.0071 0.0001 49 23 CD59 ING2 0.24 37 12 16 5 75.5% 76.2% 0.0045 2.4E−05 49 21 ADAM17 G6PD 0.24 37 10 16 5 78.7% 76.2% 2.7E−05 0.0004 47 21 GSK3B IL8 0.24 37 12 16 5 75.5% 76.2% 0.0058 0.0010 49 21 CD97 HMOX1 0.24 35 11 16 5 76.1% 76.2% 0.0026 8.9E−06 46 21 HMOX1 VIM 0.23 38 9 17 4 80.9% 81.0% 9.3E−06 0.0033 47 21 ESR1 HMOX1 0.23 38 9 16 5 80.9% 76.2% 0.0035 9.5E−05 47 21 MYD88 TNFSF5 0.23 37 10 16 5 78.7% 76.2% 0.0305 2.4E−05 47 21 TLR2 TXNRD1 0.23 36 11 16 5 76.6% 76.2% 0.0003 1.4E−05 47 21 HOXA10 LTA 0.23 41 6 17 4 87.2% 81.0% 0.0004 0.0002 47 21 IL8 SPARC 0.23 37 10 16 5 78.7% 76.2% 0.0487 0.0055 47 21 SERPINE1 TNFSF5 0.23 37 10 16 5 78.7% 76.2% 0.0338 7.8E−05 47 21 MME SPARC 0.23 39 8 16 5 83.0% 76.2% 0.0494 0.0001 47 21 HMOX1 LARGE 0.23 37 10 16 5 78.7% 76.2% 3.1E−05 0.0039 47 21 CCL5 IL8 0.23 37 10 17 4 78.7% 81.0% 0.0066 0.0227 47 21 APC ITGAL 0.23 36 11 16 5 76.6% 76.2% 1.6E−05 0.0273 47 21 IKBKE TGFB1 0.23 37 10 16 5 78.7% 76.2% 0.0002 0.0002 47 21 HOXA10 SIAH2 0.23 36 11 17 4 76.6% 81.0% 0.0055 0.0003 47 21 CAV1 ING2 0.23 38 11 16 5 77.6% 76.2% 0.0077 0.0005 49 21 IRF1 MME 0.23 39 8 16 5 83.0% 76.2% 0.0002 0.0002 47 21 MLH1 PLXDC2 0.23 37 10 16 5 78.7% 76.2% 3.1E−05 0.0014 47 21 HMOX1 NCOA1 0.23 36 11 16 5 76.6% 76.2% 1.5E−05 0.0049 47 21 CTSD TNFSF5 0.22 38 9 16 5 80.9% 76.2% 0.0467 2.0E−05 47 21 ING2 MAPK14 0.22 36 11 17 4 76.6% 81.0% 1.8E−05 0.0107 47 21 APC PTGS2 0.22 39 10 16 5 79.6% 76.2% 1.3E−05 0.0468 49 21 LTA TGFB1 0.22 36 11 16 5 76.6% 76.2% 0.0002 0.0006 47 21 CCL5 ESR1 0.22 36 11 16 5 76.6% 76.2% 0.0003 0.0364 47 21 ADAM17 RP51077B9.4 0.21 36 11 16 5 76.6% 76.2% 0.0006 0.0013 47 21 CCL5 HMOX1 0.21 37 9 16 5 80.4% 76.2% 0.0092 0.0484 46 21 CA4 PTEN 0.21 37 10 16 5 78.7% 76.2% 0.0001 0.0005 47 21 HOXA10 IKBKE 0.20 36 11 17 4 76.6% 81.0% 0.0004 0.0008 47 21 G6PD ING2 0.20 39 10 16 5 79.6% 76.2% 0.0251 0.0001 49 21 ADAM17 UBE2C 0.20 36 10 17 4 78.3% 81.0% 0.0010 0.0019 46 21 ING2 SERPINE1 0.20 40 9 16 5 81.6% 76.2% 0.0002 0.0277 49 21 BCAM SIAH2 0.20 35 11 16 5 76.1% 76.2% 0.0175 6.2E−05 46 21 IFI16 XRCC1 0.20 37 10 16 5 78.7% 76.2% 0.0002 0.0008 47 21 HMOX1 PTPRC 0.20 38 8 16 5 82.6% 76.2% 6.4E−05 0.0163 46 21 S100A4 SIAH2 0.20 36 11 16 5 76.6% 76.2% 0.0238 5.0E−05 47 21 CTNNA1 ING2 0.19 38 11 16 5 77.6% 76.2% 0.0365 6.0E−05 49 21 GSK3B HSPA1A 0.19 37 12 16 5 75.5% 76.2% 4.7E−05 0.0078 49 21 PTEN S100A11 0.19 39 8 17 4 83.0% 81.0% 0.0004 0.0003 47 21 IRF1 SP1 0.19 40 7 17 4 85.1% 81.0% 8.7E−05 0.0011 47 21 HMOX1 S100A4 0.19 38 9 17 4 80.9% 81.0% 8.3E−05 0.0327 47 21 HMOX1 PTEN 0.19 37 10 17 4 78.7% 81.0% 0.0004 0.0333 47 21 GSK3B ST14 0.18 39 10 17 4 79.6% 81.0% 0.0001 0.0123 49 21 HMOX1 USP7 0.18 36 11 16 5 76.6% 76.2% 0.0001 0.0451 47 21 CD97 IRF1 0.16 36 10 16 5 78.3% 76.2% 0.0032 0.0002 46 21 CASP9 MLH1 0.16 36 11 16 5 76.6% 76.2% 0.0294 0.0002 47 21 CCL3 MLH1 0.15 35 11 16 5 76.1% 76.2% 0.0344 0.0007 46 21 IQGAP1 IRF1 0.15 37 10 16 5 78.7% 76.2% 0.0067 0.0008 47 21 IRF1 LGALS8 0.14 35 11 16 5 76.1% 76.2% 0.0006 0.0077 46 21 GNB1 IFI16 0.14 36 11 16 5 76.6% 76.2% 0.0126 0.0007 47 21 LGALS8 TGFB1 0.13 36 11 16 5 76.6% 76.2% 0.0138 0.0012 47 21 ESR1 HOXA10 0.13 38 11 16 5 77.6% 76.2% 0.0324 0.0098 49 21 HOXA10 NUDT4 0.12 40 7 16 5 85.1% 76.2% 0.0071 0.0345 47 21

TABLE 5B Colon Normals Sum Group Size 31.5% 68.5% 100% N = 23 50 73  Gene Mean Mean p-val AXIN2 20.3 19.2 2.4E−09 CCR7 15.8 14.8 5.9E−09 MSH2 18.7 17.9 4.4E−07 MSH6 20.0 19.3 8.1E−07 CNKSR2 22.1 21.2 1.3E−06 ZNF350 19.9 19.3 1.6E−06 NBEA 22.7 21.6 2.1E−06 C1QB 19.7 21.2 2.3E−06 EGR1 18.9 19.8 3.0E−06 C1QA 19.3 20.7 6.1E−06 TNF 18.1 18.7 8.0E−06 SPARC 14.0 14.8 8.2E−05 APC 18.4 17.8 0.0001 TNFSF5 18.3 17.7 0.0001 CCL5 11.7 12.3 0.0002 IL8 22.3 21.4 0.0002 E2F1 19.5 20.2 0.0004 ING2 19.9 19.6 0.0005 SIAH2 13.1 14.0 0.0007 HMOX1 15.7 16.3 0.0009 GSK3B 16.2 15.8 0.0021 MLH1 18.1 17.8 0.0030 PTPRK 22.4 21.7 0.0042 TGFB1 12.4 12.7 0.0050 ADAM17 18.6 18.2 0.0060 CAV1 22.9 23.7 0.0072 TIMP1 14.4 14.7 0.0074 PTEN 14.2 13.8 0.0088 FOS 15.1 15.6 0.0091 TXNRD1 17.2 16.9 0.0093 LTA 19.6 19.3 0.0095 HOXA10 22.4 23.1 0.0115 UBE2C 20.4 20.8 0.0118 RP51077B9.4 16.3 16.6 0.0130 SERPING1 17.5 18.3 0.0144 IFI16 14.3 14.6 0.0178 CA4 18.5 19.1 0.0225 IRF1 12.5 12.8 0.0252 IKBKE 17.0 16.7 0.0280 MME 15.5 15.1 0.0295 NRAS 16.8 17.0 0.0309 SERPINE1 20.5 20.9 0.0339 GADD45A 19.0 19.3 0.0353 ESR1 22.3 21.9 0.0383 ESR2 24.5 23.9 0.0417 G6PD 15.4 15.7 0.0437 S100A11 11.0 11.3 0.0628 CDH1 20.1 20.4 0.0691 NUDT4 15.7 16.1 0.0732 TNFRSF1A 15.1 15.4 0.0809 ST14 17.6 17.9 0.0857 MMP9 14.1 14.6 0.0877 XRCC1 18.6 18.4 0.0960 HMGA1 15.6 15.8 0.1154 NEDD4L 18.3 18.5 0.1201 CD59 17.5 17.7 0.1205 RBM5 16.1 15.9 0.1214 MYD88 14.3 14.5 0.1359 IQGAP1 14.0 13.8 0.1550 LARGE 22.3 22.0 0.1674 MTF1 17.6 17.9 0.1794 MYC 18.3 18.1 0.1898 PLXDC2 16.6 16.7 0.1958 CCL3 20.0 20.2 0.2456 CEACAM1 18.3 18.5 0.2484 IGF2BP2 15.7 15.9 0.2504 IGFBP3 22.1 22.4 0.3151 DLC1 23.3 23.5 0.3424 XK 17.6 17.9 0.3635 PLEK2 18.2 18.5 0.3701 ANLN 22.2 22.4 0.3744 PTPRC 12.4 12.3 0.4140 ZNF185 16.9 17.0 0.4201 ITGAL 14.6 14.7 0.4241 TLR2 16.0 16.1 0.4248 BCAM 20.4 20.7 0.4396 CTSD 13.0 13.2 0.4600 S100A4 13.0 13.2 0.4606 CASP3 20.5 20.3 0.4626 SRF 16.3 16.4 0.4695 BAX 15.6 15.7 0.4717 ETS2 17.3 17.4 0.4889 CXCL1 19.8 19.7 0.5361 ACPP 18.0 17.9 0.5367 MAPK14 15.2 15.3 0.5479 LGALS8 17.5 17.4 0.5731 MEIS1 21.7 21.8 0.5828 MNDA 12.7 12.8 0.6082 PLAU 23.9 24.0 0.6255 SP1 15.8 15.7 0.6356 GNB1 13.5 13.4 0.6407 NCOA1 16.2 16.2 0.6518 CTNNA1 16.9 17.0 0.6903 DIABLO 18.5 18.5 0.6940 HSPA1A 14.5 14.5 0.7229 USP7 15.2 15.2 0.7383 DAD1 15.3 15.3 0.7470 POV1 18.2 18.2 0.7579 PTGS2 17.2 17.2 0.7953 CASP9 18.1 18.0 0.8087 SERPINA1 12.7 12.7 0.8238 TEGT 12.4 12.4 0.8779 VEGF 22.7 22.8 0.9203 MTA1 19.4 19.5 0.9261 ELA2 20.9 20.8 0.9542 VIM 11.4 11.4 0.9681 CD97 12.9 12.9 0.9862

TABLE 5C Predicted probability Patient ID Group AXIN2 TNF logit odds of colon cancer CC-010:XS:200072430 Colon Cancer 22.23 18.09 12.34 2.3E+05 1.0000 CC-007:XS:200072427 Colon Cancer 21.66 18.20 9.29 10865.66 0.9999 CC-004:XS:200072424 Colon Cancer 21.76 18.57 8.42 4538.86 0.9998 CC-008:XS:200072428 Colon Cancer 20.98 17.94 7.18 1307.55 0.9992 CC-002:XS:200072422 Colon Cancer 21.33 18.56 6.49 660.48 0.9985 CC-011:XS:200072431 Colon Cancer 20.36 17.45 6.11 449.07 0.9978 CC-003:XS:200072423 Colon Cancer 20.31 17.65 5.14 170.20 0.9942 CC-034:XS:200072442 Colon Cancer 20.18 17.64 4.59 98.65 0.9900 CC-031:XS:200072439 Colon Cancer 19.70 17.08 4.42 83.04 0.9881 CC-014:XS:200072434 Colon Cancer 20.46 18.41 3.00 20.17 0.9528 CC-006:XS:200072426 Colon Cancer 20.09 18.13 2.38 10.83 0.9155 HN-041-XS:200073106 Normal 19.78 17.89 1.85 6.35 0.8639 CC-018:XS:200072436 Colon Cancer 19.84 18.03 1.62 5.04 0.8344 CC-019:XS:200072437 Colon Cancer 20.02 18.26 1.56 4.77 0.8268 CC-013:XS:200072433 Colon Cancer 20.68 19.18 1.23 3.43 0.7742 HN-001-XS:200072922 Normal 19.95 18.32 1.04 2.83 0.7388 CC-032:XS:200072440 Colon Cancer 19.61 18.03 0.52 1.68 0.6264 CC-005:XS:200072425 Colon Cancer 20.11 18.67 0.50 1.65 0.6231 CC-033:XS:200072441 Colon Cancer 19.28 17.69 0.28 1.32 0.5686 CC-009:XS:200072429 Colon Cancer 19.20 17.62 0.15 1.16 0.5370 HN-050-XS:200073113 Normal 19.36 17.87 0.00 1.00 0.5010 CC-012:XS:200072432 Colon Cancer 20.04 18.81 −0.32 0.72 0.4197 HN-004-XS:200072925 Normal 19.54 18.23 −0.52 0.60 0.3738 HN-029-XS:200073095 Normal 20.31 19.33 −1.02 0.36 0.2647 HN-026-XS:200073092 Normal 20.17 19.24 −1.35 0.26 0.2063 HN-012-XS:200072931 Normal 19.57 18.52 −1.48 0.23 0.1855 HN-010-XS:200072930 Normal 19.13 18.06 −1.78 0.17 0.1446 HN-015-XS:200072934 Normal 19.34 18.39 −2.04 0.13 0.1153 HN-007-XS:200072927 Normal 19.50 18.60 −2.04 0.13 0.1149 HN-049-XS:200073112 Normal 19.67 18.82 −2.08 0.12 0.1111 HN-035-XS:200073100 Normal 19.41 18.52 −2.15 0.12 0.1046 HN-040-XS:200073105 Normal 19.04 18.06 −2.18 0.11 0.1014 CC-015:XS:200072435 Colon Cancer 19.55 18.71 −2.23 0.11 0.0968 HN-106-XS:200073119 Normal 19.12 18.20 −2.35 0.10 0.0873 HN-034-XS:200073099 Normal 19.26 18.40 −2.44 0.09 0.0801 HN-008-XS:200072928 Normal 19.26 18.42 −2.49 0.08 0.0766 HN-002-XS:200072923 Normal 19.52 18.76 −2.52 0.08 0.0746 HN-038-XS:200073103 Normal 19.23 18.40 −2.57 0.08 0.0708 HN-025-XS:200073091 Normal 19.40 18.67 −2.79 0.06 0.0578 HN-102-XS:200073115 Normal 18.93 18.10 −2.84 0.06 0.0554 CC-001:XS:200072421 Colon Cancer 19.05 18.26 −2.87 0.06 0.0536 HN-044-XS:200073109 Normal 19.16 18.41 −2.93 0.05 0.0507 HN-042-XS:200073107 Normal 19.06 18.29 −2.93 0.05 0.0506 HN-039-XS:200073104 Normal 18.66 17.81 −3.02 0.05 0.0466 HN-022-XS:200072948 Normal 19.95 19.45 −3.09 0.05 0.0434 HN-020-XS:200072946 Normal 19.24 18.57 −3.15 0.04 0.0410 HN-104-XS:200073117 Normal 19.29 18.73 −3.48 0.03 0.0300 HN-019-XS:200072945 Normal 19.05 18.45 −3.57 0.03 0.0274 HN-027-XS:200073093 Normal 19.19 18.65 −3.67 0.03 0.0249 HN-045-XS:200073110 Normal 19.18 18.67 −3.76 0.02 0.0227 HN-014-XS:200072933 Normal 18.90 18.32 −3.77 0.02 0.0224 HN-016-XS:200072935 Normal 18.98 18.42 −3.80 0.02 0.0219 HN-030-XS:200073096 Normal 19.67 19.32 −3.92 0.02 0.0194 HN-017-XS:200072936 Normal 19.11 18.68 −4.15 0.02 0.0156 HN-032-XS:200073097 Normal 19.30 18.99 −4.41 0.01 0.0120 HN-105-XS:200073118 Normal 19.23 18.95 −4.59 0.01 0.0101 HN-047-XS:200073111 Normal 18.79 18.44 −4.73 0.01 0.0087 HN-033-XS:200073098 Normal 19.77 19.74 −5.01 0.01 0.0066 HN-036-XS:200073101 Normal 18.95 18.76 −5.19 0.01 0.0055 HN-018-XS:200072944 Normal 18.94 18.78 −5.29 0.01 0.0050 HN-005-XS:200072926 Normal 18.83 18.80 −5.87 0.00 0.0028 HN-037-XS:200073102 Normal 18.62 18.56 −5.94 0.00 0.0026 HN-101-XS:200073114 Normal 18.74 18.75 −6.07 0.00 0.0023 HN-009-XS:200072929 Normal 19.09 19.30 −6.50 0.00 0.0015 HN-003-XS:200072924 Normal 18.25 18.27 −6.57 0.00 0.0014 HN-103-XS:200073116 Normal 18.53 18.71 −6.90 0.00 0.0010 HN-024-XS:200073090 Normal 19.26 19.73 −7.33 0.00 0.0007 HN-028-XS:200073094 Normal 19.47 20.03 −7.43 0.00 0.0006 HN-107-XS:200073120 Normal 18.44 18.95 −8.18 0.00 0.0003 HN-021-XS:200072947 Normal 18.26 19.27 −10.20 0.00 0.0000 

1. A method for evaluating the presence of colon cancer in a subject based on a sample from the subject, the sample providing a source of RNAs, comprising: a) determining a quantitative measure of the amount of at least one constituent of any constituent of any one table selected from the group consisting of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent in the subject sample, wherein such measure is obtained under measurement conditions that are substantially repeatable and the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy; and b) comparing the quantitative measure of the constituent in the subject sample to a reference value.
 2. A method for assessing or monitoring the response to therapy in a subject having colon cancer based on a sample from the subject, the sample providing a source of RNAs, comprising: a) determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent, wherein such measure is obtained under measurement conditions that are substantially repeatable to produce subject data set; and b) comparing the subject data set to a baseline data set.
 3. A method for monitoring the progression of colon cancer in a subject, based on a sample from the subject, the sample providing a source of RNAs, comprising: a) determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent in a sample obtained at a first period of time, wherein such measure is obtained under measurement conditions that are substantially repeatable to produce a first subject data set; b) determining a quantitative measure of the amount of at least one constituent of any constituent of Tables 1, 2, 3, 4, and 5 as a distinct RNA constituent in a sample obtained at a second period of time, wherein such measure is obtained under measurement conditions that are substantially repeatable to produce a second subject data set; and c) comparing the first subject data set and the second subject data set.
 4. A method for determining a colon cancer profile based on a sample from a subject known to have colon cancer, the sample providing a source of RNAs, the method comprising: a) using amplification for measuring the amount of RNA in a panel of constituents including at least 1 constituent from Tables 1, 2, 3, 4, and 5 and b) arriving at a measure of each constituent, wherein the profile data set comprises the measure of each constituent of the panel and wherein amplification is performed under measurement conditions that are substantially repeatable.
 5. The method of any one of claims 1-4, wherein said constituent is selected from the group consisting of AXIN2, C1QA, CDKN2A, CCR7, CNKSR2, C1QB, EGR1, MSH2, MSH6 and RHOC.
 6. The method of any one of claims 1-4, comprising measuring at least two constituents from a) Table 1, wherein the first constituent is selected from the group consisting of ACSL5, ALDH1A1, APC, AXIN2, BAX, CA4, CCND3, CD44, CD63, CFLAR, GADD45A, IGFBP4, ITGA3, MGMT, MSH2, and MSH6 and the second constituent is any other constituent selected from Table 1, wherein the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy; b) Table 2, wherein the first constituent is selected from the group consisting of ADAM17, ALOX5, APAF1, C1QA, CASP1, CASP3, CCL3, CCL5, CCR5, CD19, CD4, CD8A, CTLA4, CXCL1, CXCR3, DPP4, EGR1, GZMB, HLADRA, HMOX1, HSPA1A, ICAM1, IFI16, IFNG, IL10, IL18, IL18BP, IL1B, IL1R1, IL1RN, IL23A, IL32, IL8, IRF1, LTA, MAPK14, MHC2TA, MIF, MMP9, MNDA, MYC, NFKB1, PLA2G7, PLAUR, PTGS2, PTPRC, SERPINA1, SSI3, TGFB1, TIMP1, TLR2, TNF, and TNFRSF1A, and the second constituent is any other constituent selected from Table 2, wherein the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy; c) Table 3 wherein the first constituent is selected from the group consisting of ABL1, ABL2, AKT1, APAF1, ATM, BAD, BAX, BCL2, BRAF, BRCA1, CASP8, CDK2, CDK4, CDK5, CDKN1A, CDKN2A, CFLAR, COL18A1, E2F1, EGR1, ERBB2, FOS, GZMA, HRAS, IFITM1, IL1B, IL8, ITGA1, ITGA3, ITGAE, ITGB1, MMP9, MSH2, MYC, MYCL1, NFKB1, NME4, NOTCH2, NRAS, PCNA, PLAUR, PTCH1, RB1, RHOA, RHOC, S100A4, SEMA4D, SERPINE1, SKI, SKIL, SMAD4, TGFB1, and TNF and the second constituent is any other constituent selected from Table 3, wherein the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy; d) Table 4 wherein the first constituent is selected from the group consisting of CEBPB, CREBBP, EGR1, EGR2, FOS, ICAM1, MAP2K1, NAB1, NFKB1, NR4A2, SRC, TGFB1, and TOPBP1 and the second constituent is from the group consisting of NAB1, NR4A2, PDGFA, PTEN, TGFB1, TNFRSF6, and TOPBP1, wherein the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy; and e) Table 5 wherein the first constituent is selected from the group consisting of ADAM17, APC, AXIN2, BAX, BCAM, C1QA, C1QB, CA4, CASP9, CAV1, CCL3, CCL5, CCR7, CD59, CD97, CNKSR2, CTNNA1, CTSD, DAD1, DIABLO, E2F1, EGR1, ESR1, ETS2, FOS, G6PD, GNB1, GSK3B, HMGA1, HMOX1, HOXA10, IFI16, IGF2BP2, IKBKE, IL8, ING2, IQGAP1, IRF1, ITGAL, LARGE, LGALS8, LTA, MAPK14, MLH1, MME, MMP9, MNDA, MSH2, MSH6, MTA1, MTF1, MYD88, NBEA, NCOA1, NRAS, PLEK2, PLXDC2, PTEN, PTPRK, RBM5, S100A4, SERPINE1, SERPING1, SIAH2, SPARC, SRF, ST14, TGFB1, TIMP1, TLR2, TNF, TNFRSF1A, TNFSF5, and UBE2C and the second constituent is any other constituents selected from Table 5, wherein the constituent is selected so that measurement of the constituent distinguishes between a normal subject and a colon cancer-diagnosed subject in a reference population with at least 75% accuracy.
 7. The method of any one of claims 1-6, wherein the combination of constituents are selected according to any of the models enumerated in Tables 1A, 2A, 3A, 4A, or 5A.
 8. The method of any one of claims 1, 5 and 6, wherein said reference value is an index value.
 9. The method of claim 2, wherein said therapy is immunotherapy.
 10. The method of claim 9, wherein said constituent is selected from Table
 6. 11. The method of any one of claim 2, 9 or 10, wherein when the baseline data set is derived from a normal subject a similarity in the subject data set and the baseline date set indicates that said therapy is efficacious.
 12. The method of any one of claim 2, 9 or 10, wherein when the baseline data set is derived from a subject known to have colon cancer a similarity in the subject data set and the baseline date set indicates that said therapy is not efficacious.
 13. The method of any one of claims 1-12, wherein expression of said constituent in said subject is increased compared to expression of said constituent in a normal reference sample.
 14. The method of any one of claims 1-12, wherein expression of said constituent in said subject is decreased compared to expression of said constituent in a normal reference sample.
 15. The method of any one of claims 1-12, wherein the sample is selected from the group consisting of blood, a blood fraction, a body fluid, a cells and a tissue.
 16. The method of any one of claims 1-15, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than ten percent.
 17. The method of any one of claims 1-16, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than five percent.
 18. The method of any one of claims 1-17, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than three percent.
 19. The method of any one of claims 1-18, wherein efficiencies of amplification for all constituents are substantially similar.
 20. The method of any one of claims 1-19, wherein the efficiency of amplification for all constituents is within ten percent.
 21. The method of any one of claims 1-20, wherein the efficiency of amplification for all constituents is within five percent.
 22. The method of any one of claims 1-19, wherein the efficiency of amplification for all constituents is within three percent.
 23. A kit for detecting colon cancer in a subject, comprising at least one reagent for the detection or quantification of any constituent measured according to any one of claims 1-22 and instructions for using the kit. 